Publications #

• The copyrights of published papers are transferred to publishers. Misuse of any material posted here may result in plagiarism.
• + means my current/previous student or visitor.
• * means equal contribution

To appear #

Technical Report #

• A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II-A Data Science Perspective
  Mingyu Huang+, Ke Li
  CoRR abs/2404.14228 | April 2024
  PDF | Supp | BiB

• Multi-Fidelity Methods for Optimization: A Survey
  Ke Li, Fan Li+
  CoRR abs/2402.09638 | February 2024
  PDF | Supp | BiB

• Unlocking the Secrets of Software Configuration Landscapes-Ruggedness, Accessibility, Escapability, and Transferability
  Mingyu Huang+, Peili Mao+, Ke Li
  CoRR abs/2201.01429 | February 2024
  PDF | Supp | BiB

• Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning
  Ke Li, Han Guo+
  CoRR abs/2401.02160 | January 2024
  PDF | Supp | BiB

• Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
  Shengbo Wang+, Ke Li, Yin Yang, Yuting Cao, Tingwen Huang, Shiping Wen
  CoRR abs/2307.00828 | July 2023
  PDF | Supp | BiB

• Evolutionary Multi-Objective Optimization for Virtual Network Function Placement
  Joseph Billingsley+, Ke Li, Geyong Min, Nektarios Georgalas
  CoRR abs/2106.14727 | March 2023
  PDF | Supp | BiB

  @article{BillingsleyLMMG22,
    author    = {Joseph Billingsley and
                 Ke Li and
                 Wang Miao and
                 Geyong Min and
                 Nektarios Georgalas},
    title     = {Evolutionary Multi-Objective Virtual Network Function Placement: {A}
                 Formal Model and Effective Algorithms},
    journal   = {CoRR},
    volume    = {abs/2106.14727},
    year      = {2021},
    url       = {https://arxiv.org/abs/2106.14727},
    eprinttype = {arXiv},
    eprint    = {2106.14727},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2106-14727.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• LONViZ: Unboxing the Black-Box of Configurable Software Systems from a Complex Networks Perspective
  Ke Li, Peili Mao+, Tao Chen
  CoRR abs/2201.01429 | January 2022
  PDF | BiB

  @article{LiMC22,
    author    = {Ke Li and
                 Peili Mao and
                 Tao Chen},
    title     = {LONViZ: Unboxing the black-box of Configurable Software Systems from
                 a Complex Networks Perspective},
    journal   = {CoRR},
    volume    = {abs/2201.01429},
    year      = {2022},
    url       = {https://arxiv.org/abs/2201.01429},
    eprinttype = {arXiv},
    eprint    = {2201.01429},
    timestamp = {Mon, 10 Jan 2022 13:39:01 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2201-01429.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

In press #

• Rethinking Performance Analysis for Configurable Software Systems: A Case Study from a Fitness Landscape Perspective
  Mingyu Huang+, Peili Mao+, Ke Li
  Proc. of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2025)
  Abs | PDF | Code | BiB

• Bridging Sequence-Structure Alignment in RNA Foundation Models
  Heng Yang+, Renzhi Chen, Ke Li
  Proc. of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)
  Abs | PDF | Code | BiB | ≈ 23.4%

• On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study
  Mingyu Huang+, Ke Li
  Proc. of the 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2025)
  Abs | PDF | Code | BiB | ≈ 19.0%

• A Survey of Multi-objective Evolutionary Algorithm Based on Decomposition: Past and Future
  Ke Li
  IEEE Trans. Evolutionary Computation (TEVC)
  10.1109/TEVC.2024.3496507
  PDF | Supp | BiB

• Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
  Tian Huang+, Shengbo Wang+, Ke Li
  Proc. of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024)
  Abs | PDF | Code | BiB | ≈ 25.8%

• Evolutionary Alternating Direction Method of Multipliers for Constrained Multi-Objective Optimization with Unknown Constraints
  Shuang Li+, Ke Li, Wei Li, Ming Yang
  IEEE Trans. Evolutionary Computation (TEVC)
  10.1109/TEVC.2024.3425629
  Abs | PDF | BiB

• Mutual Knowledge Distillation based Personalized Federated Learning for Smart Edge Computing
  Siwei Zheng, Jia Hu, Geyong Min, Ke Li
  IEEE Trans. Consumer Electronics (TCE)
  10.1109/TCE.2024.3412817
  Abs | PDF | BiB

• Evolutionary Art Attack For Black-Box Adversarial Example Generation
  Phoenix Williams+, Ke Li, G. Min
  IEEE Trans. Evolutionary Computation (TEVC)
  10.1109/TEVC.2024.3391063
  Abs | PDF | Code | BiB

• DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls
  Ke Li, Heng Yang+, Willem Visser
  IEEE Trans. Software Engineering (TSE)
  10.1109/TSE.2023.3343716
  Abs | PDF | Code | BiB

  Web application firewall (WAF) plays an integral role nowadays to protect web applications from various malicious injection attacks such as SQL injection, XML injection, and PHP injection, to name a few. However, given the evolving sophistication of injection attacks and the increasing complexity of tuning a WAF, it is challenging to ensure that the WAF is free of injection vulnerabilities such that it will block all malicious injection attacks without wrongly affecting the legitimate message. Automatically testing the WAF is, therefore, a timely and essential task. In this paper, we propose DANUOYI, an automatic injection testing tool that simultaneously generates test inputs for multiple types of injection attacks on a WAF. Our basic idea derives from the cross-lingual translation in the natural language processing domain. In particular, test inputs for different types of injection attacks are syntactically different but may be semantically similar. Sharing semantic knowledge across multiple programming languages can thus stimulate the generation of more sophisticated test inputs and discovering injection vulnerabilities of the WAF that are otherwise difficult to find. To this end, in DANUOYI, we train several injection translation models by using multi-task learning that translates the test inputs between any pair of injection attacks. The model is then used by a novel multi-task evolutionary algorithm to co-evolve test inputs for different types of injection attacks facilitated by a shared mating pool and domain-specific mutation operators at each generation. We conduct experiments on three real-world open-source WAFs and six types of injection attacks, the results reveal that DANUOYI generates up to 3.8x and 5.78x more valid test inputs (i.e., bypassing the underlying WAF) than its state-of-the-art single-task counterparts and the context-free grammar-based injection construction.
  

  @article{LiYV23,
      author    = {Ke Li and
                   Heng Yang and
                   Willem Visser},
      title     = {DaNuoYi: Evolutionary Multi-Task Injection Testing on Web Application Firewalls},
      journal   = {IEEE Trans. Softw. Eng.},
      year      = {2023},
      note      = {accepted for publication}
  }
  

2024 #

Journal #

• An Interpretable RNA Foundation Model for Exploration Functional RNA Motifs in Plants
  Haopeng Yu, Heng Yang+, Wenqing Sun, Zongyun Yan, Xiaofei Yang, Huakun Zhang, Yiliang Ding, Ke Li
  10.1038/s42256-024-00946-z
  Nature Machine Intelligence (NMI), 6: 1616–1625, 2024
  PDF | Project | Code | Supp | BiB

• Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis
  Ryoji Tanabe, Ke Li
  IEEE Trans. Evolutionary Computation (TEVC), 28(6): 1575–1589, 2024
  10.1109/TEVC.2023.3319009
  Abs | PDF | Supp | BiB

  Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both benchmarking and practical decision-making, neither has been conducted. In this context, first, this paper reviews existing regions of interest and quality indicators for preference-based evolutionary multi-objective optimization using the reference point. We point out that each quality indicator was designed for a different region of interest. Then, this paper investigates the properties of the quality indicators. We demonstrate that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space. We observe that the regions of interest can be significantly different depending on the position of the reference point and the shape of the Pareto front. We identify undesirable properties of some quality indicators. We also show that the ranking of preference-based evolutionary multi-objective optimization algorithms depends on the choice of quality indicators.
  

  @article{TanabeL23,
      author    = {Ryoji Tanabe and
                   Ke Li},
      title     = {Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis},
      journal   = {IEEE Trans. Evol. Comput.},
      year      = {2023},
      note      = {accepted for publication}
  }
  

• Solving Expensive Optimization Problems in Dynamic Environments with Meta-Learning
  Huan Zhang+, Jinliang Ding, Liang Feng, Kay Chen Tan, Ke Li
  IEEE Trans. Cybernetics (TCYB), 52(12): 7430–7442, 2024
  10.1109/TCYB.2024.3443396
  Abs | PDF | BiB

• A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
  Ke Li*, Renzhi Chen+*, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 28(5): 1396–1411, 2024
  10.1109/TEVC.2023.3307244
  Abs | PDF | Supp | Code | BiB

  Many real-world problems are computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach to tackle expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple yet effective transfer learning framework to empower data-driven evolutionary optimization to solve expensive dynamic optimization problems. Specifically, a hierarchical multi-output Gaussian process is proposed to capture the correlation among data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization processes. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a very limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm in comparison with nine state-of-the-art peer algorithms.
  

  @article{LiCY23,
      author    = {Ke Li and
                   Renzhi Chen and
                   Xin Yao},
      title     = {A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments},
      journal   = {IEEE Trans. Evol. Comput.},
      year      = {2023},
      url       = {https://ieeexplore.ieee.org/document/10225543},
      doi       = {10.1109/TEVC.2023.3307244}
  }
  
  

• A Many-Objective Evolutionary Algorithm Based on Interaction Force and Hybrid Optimization Mechanism
  Lei Yang+, Jiale Cao, Kangshun Li, Yuanye Zhang, Rui Xu, Ke Li
  Swarm and Evolutionary Computation (SWEVO), 90: 101667
  https://doi.org/10.1016/j.swevo.2024.101667
  Abs | PDF | BiB

• iM-Seeker: A Webserver for DNA I-motifs Prediction and Scoring via Automated Machine Learning
  Haopeng Yu, Fan Li+, Bibo Yang, Yiman Qi, Dilek Guneri, Wenqian Chen, Zoe Waller, Ke Li, Yiliang Ding
  Nucleic Acids Research (NAR), 52(W1): W19-W28, 2024
  https://doi.org/10.1093/nar/gkae315
  Abs | PDF | Code | BiB

• Multi-Output Framework for Time-Series Forecasting in Smart Grid Meets Data Scarcity
  Jiangjiao Xu+, Ke Li
  IEEE Trans. Industrial Informatics (TII), 20(9): 11202–11212, 2024
  10.1109/TII.2024.3396347
  Abs | PDF | BiB

• A Knee Point Driven Evolutionary Algorithm for Multi-Objective Bilevel Optimization
  Jiaxin Chen+, Jinliang Ding, Ke Li, Kay Chen Tan, Tianyou Chai
  IEEE Trans. Cybernetics (TCYB), 54(7): 4177–4189, 2024
  10.1109/TCYB.2024.3377272
  Abs | PDF | Code | BiB

• An Automated Few-Shot Learning for Time Series Forecasting in Smart Grid Under Data Scarcity
  Jiangjiao Xu+, Ke Li, D. Li
  IEEE Trans. Artificial Intelligence (TAI), 5(6): 2482–2492, 2024
  10.1109/TAI.2024.3358795
  Abs | PDF | Code | BiB

• Evolutionary Bi-level Optimization via Multi-objective Transformation Based Lower Level Search
  Lei Chen, Hai-Lin Liu, Ke Li, Kay Chen Tan
  IEEE Trans. Evolutionary Computation (TEVC), 28(3): 733–747, 2024
  10.1109/TEVC.2023.3236455
  Abs | PDF | BiB

  Nested evolutionary algorithms (EAs) have been regarded as very promising tools for bi-level optimization. Due to the nested structure, the upper level population evaluation requires a set of complete lower level optimizations, thereby reducing the efficiency and practicability of EA methods. In this paper, a multi-objective transformation-based evolutionary algorithm (MOTEA) is proposed to perform multiple lower level optimizations in a parallel and collaborative manner. Specifically, the corresponding multiple lower level optimizations for each generation of the upper level population evaluation are transformed into locating a set of Pareto optimal solutions of a constructed multi-objective optimization problem. By utilizing the built-in implicit parallelism of evolutionary multi-objective optimization, multiple lower level problems can thus be optimized in parallel. Within one multi-objective search population, the collaboration among the parallel lower level optimization can be realized by exploiting and utilizing the implicit similarities among them for better efficiency. The effectiveness and efficiency of the proposed MOTEA are verified by comparing it with four state-of-the-art evolutionary bi-level optimization algorithms on two sets of popular bi-level optimization benchmark test problems and three application problems.
  

Conference #

• MP-RNA: Unleashing Multi-species RNA Foundation Model via Calibrated Secondary Structure Prediction
  Heng Yang+, Ke Li
  Findings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)
  Abs | PDF | Code | BiB | ≈ 16.9%

• The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples
  Heng Yang+, Ke Li
  Proc. of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024)
  Abs | PDF | Code | BiB | ≈ 20.8%

• OpenTOS: Open-source System for Transfer Learning Bayesian Optimization
  Peili Mao+, Ke Li
  Proc. of the 33rd ACM International Conference on Information and Knowledge Management (CIKM 2024) Demo Paper track
  Abs | PDF | BiB | ≈ 43.2%

• RNAInvBench: Benchmark for the RNA Inverse Design Problem
  Jack Cole+, Fan Li+, Liwen Wu+, Krasmira Tsaneva-Atanasova, Ke Li
  Proc. of the 2024 ICML AI for Science Workshop
  Abs | PDF | Code | BiB

• Evolutionary Multi-Objective Optimization for Contextual Adversarial Example Generation
  Shasha Zhou+, Mingyu Huang+, Yanan Sun, Ke Li
  Proc. of the 2024 ACM International Conference on the Foundations of Software Engineering (FSE'24), 101: 2285–2308, July, 2024
  Abs | PDF | Code | BiB | ≈ 21.0%

• Constrained Bayesian Optimization Under Partial Observations: Balanced Improvements and Provable Convergence
  Shengbo Wang+, Ke Li
  10.1609/aaai.v38i14.29488
  Proc. of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI'24), 38(14): 15607-15615, February, 2024
  Abs | PDF | Code | BiB | ≈ 23.5%

  The partially observable constrained optimization problems (POCOPs) impede data-driven optimization techniques since an infeasible solution of POCOPs can provide little information about the objective as well as the constraints. We endeavor to design an efficient and provable method for expensive POCOPs under the framework of constrained Bayesian optimization. Our method consists of two key components. Firstly, we present an improved design of the acquisition functions that introduces balanced exploration during optimization. We rigorously study the convergence properties of this design to demonstrate its effectiveness. Secondly, we propose a Gaussian process embedding different likelihoods as the surrogate model for a partially observable constraint. This model leads to a more accurate representation of the feasible regions compared to traditional classification-based models. Our proposed method is empirically studied on both synthetic and real-world problems. The results demonstrate the competitiveness of our method for solving POCOPs.
  

  @inproceedings{WangL23,
      author       = {Shengbo Wang and
                      Ke Li},
      title        = {Constrained {Bayesian} Optimization Under Partial Observations: Balanced Improvements and Provable Convergence},
      booktitle    = {AAAI'24: Proc. of the Thirty-Eighth AAAI Conference on Artificial Intelligence},
      year         = {2024},
      note         = {accepted for publication},
      doi          = {https://doi.org/10.48550/arXiv.2312.03212},
      url          = {https://arxiv.org/abs/2312.03212}
  }
  

• Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation
  Heng Yang+, Ke Li
  https://aclanthology.org/2024.findings-eacl.13
  Findings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL'24), p. 182-195, March, 2024.
  Abs | PDF | BiB

2023 #

Journal #

• Multidimensional Resource Fragmentation-Aware Virtual Network Embedding for IoT Applications in MEC Networks
  Yingying Guan+, Qingyang Song, Weijing Qi, Lei Guo, Ke Li, Abbas Jamalipour
  IEEE Internet of Things Journal (IoTJ), 10(24): 22223–22232, 2023
  10.1109/JIOT.2023.3304976
  Abs | PDF | BiB

  The proliferation of Internet of Things (IoT) applications has led to the interconnection of multiaccess edge computing (MEC) systems through metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, the uneven utilization of multidimensional resources can result in resource fragmentation, thereby reducing the utilization of limited edge resources. This article focuses on mitigating multidimensional resource fragmentation in virtual network embedding (VNE) to maximize the profit of the infrastructure provider (InP). The problem is converted into a bilevel optimization problem, taking into account the interdependence between virtual node embedding and virtual link embedding. To solve this problem, we propose a nested bilevel VNE approach named BiVNE. BiVNE leverages an ant colony system (ACS) algorithm for the upper layer problem and utilizes the Dijkstra algorithm and an exact-fit spectrum slot assignment method for the lower layer problem. Evaluation results demonstrate that BiVNE can greatly improve the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously.
  

  @article{GuanSQLGJ23,
      author    = {Yingying Guan and
                   Qingyang Song and
                   Weijing Qi and
                   Ke Li and
                   Lei Guo and
                   Abbas Jamalipour},
      title     = {Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in {MEC} Systems Interconnected by Metro Optical Networks},
      journal   = {IEEE Internet of Things Journal},
      volume    = {10},
      number    = {24}
      year      = {2023},
      url       = {https://ieeexplore.ieee.org/document/10217060?source=authoralert},
      doi       = {10.1109/JIOT.2023.3304976}
  }
  

• Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank
  Ke Li, Guiyu Lai+, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 27(4): 749–763, 2023
  10.1109/TEVC.2023.3234269
  Abs | PDF | Supp | BiB

  In practical multi-criterion decision-making, it is cumbersome if a decision maker (DM) is asked to choose among a set of trade-off alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multi-objective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multi-objective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple conflicting criteria. Bearing this in mind, this paper develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on 48 benchmark test problems with up to 10 objectives and a real-world multi-objective robot control problem fully demonstrate the effectiveness of our proposed algorithms for finding SOI.
  

  @article{LiLY23,
      author       = {Ke Li and
                      Guiyu Lai and
                      Xin Yao},
      title        = {Interactive Evolutionary Multiobjective Optimization via Learning to Rank},
      journal      = {{IEEE} Trans. Evol. Comput.},
      volume       = {27},
      number       = {4},
      pages        = {749--763},
      year         = {2023},
      url          = {https://doi.org/10.1109/TEVC.2023.3234269},
      doi          = {10.1109/TEVC.2023.3234269},
      timestamp    = {Fri, 18 Aug 2023 08:46:27 +0200},
      biburl       = {https://dblp.org/rec/journals/tec/0001L023.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars
  Bo Lyu+, Maher Hamdi, Yin Yang, Yuting Cao, Zheng Yan, Ke Li, Shiping Wen, Tingwen Huang
  IEEE Trans. Emerging Topics in Computational Intelligence (TETCI), 7(2): 415–425, 2023
  10.1109/TETCI.2022.3210998
  Abs | PDF | BiB

  Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model. In this paper, we propose a framework for implementing batched data-driven evolutionary multi-objective optimization. It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush-Kuhn-Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set; and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against 7 state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various PF shapes.
  

  @article{LyuHYCYLSH22,
      author  = {Bo Lyu and
                 Maher Hamdi and
                 Yin Yang and
                 Yuting Cao and
                 Zheng Yan and
                 Ke Li and
                 Shiping Wen and
                 Tingwen Huang},
      journal = {IEEE Trans. Emerg. Top. Comput. Intell.},
      title   = {Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars}, 
      year    = {2022},
      pages   = {1-11},
      note    = {accepted for publication},
      doi     = {10.1109/TETCI.2022.3210998}
  }
  

• Neural Architecture Search for Portrait Parsing
  Bo Lyu+, Yin Yang, Shiping Wen, Tingwen Huang, Ke Li
  IEEE Trans. Neural Networks and Learning Systems (TNNLS), 34(3): 1112–1121, 2023
  10.1109/TNNLS.2021.3104872
  Abs | PDF | BiB

  This work proposes a neural architecture search (NAS) method for portrait parsing, which is a novel up-level task based on portrait segmentation and face labeling. Recently, NAS has become an effective method in terms of automatic machine learning. However, remarkable achievements have been made only in image classification and natural language processing (NLP) areas. Meanwhile, state-of-the-art portrait segmentation and face labeling approaches are all manually designed, but few models reach a tradeoff between efficiency and performance. Thus, we are extremely interested in improving existing NAS methods for dense-per-pixel prediction tasks on portrait datasets. To achieve that, we resort to a cell-based encoder-decoder architecture with an elaborate design of connectivity structure and searching space. As a result, we achieve state-of-the-art performance on three portrait tasks, including 96.8% MIOU on EG1800 (portrait segmentation), 91.2% overall F1-score on HELEN (face labeling), and 95.1% overall F1-score on CelebAMask-HQ (portrait parsing) with only 2.29M model parameters. That is, our approach compares favorably with all previous works on portrait datasets. More crucially, we empirically prove that even a fundamental encoder-decoder architecture may reach an outstanding result on the aforementioned tasks with the help of the innovative approach of NAS. To the best of our knowledge, our work is also the first to report the success of applying NAS on these portrait tasks.
  

  @article{LyuYWHL23,
      author       = {Bo Lyu and
                      Yin Yang and
                      Shiping Wen and
                      Tingwen Huang and
                      Ke Li},
      title        = {Neural Architecture Search for Portrait Parsing},
      journal      = {{IEEE} Trans. Neural Networks Learn. Syst.},
      volume       = {34},
      number       = {3},
      pages        = {1112--1121},
      year         = {2023},
      url          = {https://doi.org/10.1109/TNNLS.2021.3104872},
      doi          = {10.1109/TNNLS.2021.3104872},
      timestamp    = {Sat, 11 Mar 2023 00:12:14 +0100},
      biburl       = {https://dblp.org/rec/journals/tnn/LyuYWHL23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Batched Data-Driven Evolutionary Multi-Objective Optimization Based on Manifold Interpolation
  Ke Li*, Renzhi Chen+*
  IEEE Trans. Evolutionary Computation (TEVC), 27(1): 126–140, 2023
  10.1109/TEVC.2022.3162993
  Abs | PDF | Supp | BiB

  Multi-objective optimization problems are ubiquitous in real-world science, engineering and design optimization problems. It is not uncommon that the objective functions are as a black box, the evaluation of which usually involve time-consuming and/or costly physical experiments. Data-driven evolutionary optimization can be used to search for a set of non-dominated trade-off solutions, where the expensive objective functions are approximated as a surrogate model. In this paper, we propose a framework for implementing batched data-driven evolutionary multi-objective optimization. It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner. There are two unique components: 1) based on the Karush-Kuhn-Tucker conditions, a manifold interpolation approach that explores more diversified solutions with a convergence guarantee along the manifold of the approximated Pareto-optimal set; and 2) a batch recommendation approach that reduces the computational time of the data-driven evolutionary optimization process by evaluating multiple samples at a time in parallel. Comparing against 7 state-of-the-art surrogate-assisted evolutionary algorithms, experiments on 168 benchmark test problem instances with various properties and a real-world application on hyper-parameter optimization fully demonstrate the effectiveness and superiority of our proposed framework, which is featured with a faster convergence and a stronger resilience to various PF shapes.
  

  @article{LiC23,
      author       = {Ke Li and
                      Renzhi Chen},
      title        = {Batched Data-Driven Evolutionary Multiobjective Optimization Based on Manifold Interpolation},
      journal      = {{IEEE} Trans. Evol. Comput.},
      volume       = {27},
      number       = {1},
      pages        = {126--140},
      year         = {2023},
      url          = {https://doi.org/10.1109/TEVC.2022.3162993},
      doi          = {10.1109/TEVC.2022.3162993},
      timestamp    = {Sat, 25 Feb 2023 21:35:23 +0100},
      biburl       = {https://dblp.org/rec/journals/tec/LiC23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• MTLP-JR: Multi-Task Learning-Based Prediction for Joint Ranking in Neural Architecture Search
  Bo Lyu+, Longfei Lu, Maher Hamdi, Shiping Wen, Yin Yang, Ke Li
  Computers & Electrical Engineering, 105: 108474, 2023
  10.1016/j.compeleceng.2022.108474
  Abs | PDF | BiB

  At present, great attentions have been paid to multi-objective neural architecture search (NAS) and resource-aware NAS for their comprehensive consideration of the overall evaluation of architectures, including inference latency, precision, and model scale. However NAS also exacerbates the ever-increasing cost (engineering, time complexity, computation resource). Aiming to alleviate this, the reproducible NAS research releases the benchmark, which includes the metrics (e.g. Accuracy, Latency, and Parameters) of representative models from the typical search space on specific tasks. Motivated by the multi-objective NAS, resource-aware NAS, and reproducible NAS, this paper dedicates to binary-relation prediction (Latency, Accuracy), which is a more reasonable and effective way to satisfy the general NAS scenarios with less cost. We conduct a reproducible NAS study on the MobileNet-based search space and release the dataset. Further, we first propose the modeling of common features among prediction tasks (Latency, Accuracy, Parameters, and FLOPs), which will facilitate the prediction of individual tasks, and creatively formulate the architecture ranking prediction with a multi-task learning framework. Eventually, the proposed multi-task learning based binary-relation prediction model reaches the performance of 94.3% on Latency and 85.02% on Top1 Accuracy even with only 100 training points, which outperforms the single-task learning based model.
  

  @article{LyuLHWYL23,
      author       = {Bo Lyu and
                      Longfei Lu and
                      Maher Hamdi and
                      Shiping Wen and
                      Yin Yang and
                      Ke Li},
      title        = {{MTLP-JR:} Multi-task learning-based prediction for joint ranking
          in neural architecture search},
      journal      = {Comput. Electr. Eng.},
      volume       = {105},
      pages        = {108474},
      year         = {2023},
      url          = {https://doi.org/10.1016/j.compeleceng.2022.108474},
      doi          = {10.1016/J.COMPELECENG.2022.108474},
      timestamp    = {Mon, 26 Jun 2023 20:54:11 +0200},
      biburl       = {https://dblp.org/rec/journals/cee/LyuLHWYL23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Multioutput Surrogate Assisted Evolutionary Algorithm for Expensive Multi-Modal Optimization Problems
  Renzhi Chen+, Ke Li
  Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC'23)
  Abs | PDF | BiB

• Empirical Studies of Resampling Strategies in Noisy Evolutionary Multi-Objective Optimization
  Shasha Zhou+, Ke Li
  Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC'23)
  Abs | PDF | BiB

• Preference-Based Multi-Objective Optimization with Gaussian Process
  Tian Huang+, Ke Li
  Proc. of the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC'23)
  Abs | PDF | BiB

• A Multi-Population Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multi-Objective Problems with Multi-Constraint
  Juan Zou, Ruiqing Sun, Yuan Liu, Yaru Hu, Shengxiang Yang, Jinhua Zheng, Ke Li
  IEEE Trans. Evolutionary Computation (TEVC)
  10.1109/TEVC.2023.3260306
  Abs | PDF | BiB

  In science and engineering, multi-objective optimization problems usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This paper aims to solve the challenges brought by multiple complex constraints. First, this paper analyzes the relationship between single constrained Pareto Front (SCPF) and their common Pareto Front sub-constrained Pareto Front (SubCPF). Next, we discussed the SCPF, SubCPF, and Unconstrainti Pareto Front (UPF)’s help to solve constraining Pareto Front (CPF). Then further discusses what kind of cooperation should be used between multiple populations constrained multi-objective optimization algorithm (CMOEA) to better deal with multi-constrained multi-objective optimization problems (mCMOPs). At the same time, based on the discussion in this paper, we propose a new multi-population CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed Activation Dormancy Detection (ADD) to accelerate the optimization process and uses the proposed Combine Occasion Detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.
  

• “Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization
  Shuang Li+, Ke Li, Wei Li
  Proc. of the 37th Conference on Neural Information Processing Systems (NeurIPS'23)
  Abs | PDF | Code | BiB | ≈ 26.1%

  Bayesian Optimization (BO) is a powerful method for tackling expensive black-box optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to approximately 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.
  
  

  @inproceedings{LiLL23,
    author       = {Shuang Li and
                    Ke Li and
                    Wei Li},
    title        = {“Why Not Looking backward?” A Robust Two-Step Method to Automatically Terminate Bayesian Optimization},
    booktitle    = {NeurIPS'23: Proc. of 37th Conference on Neural Information Processing Systems},
    pages        = {1--12},
    year         = {2023},
    url          = {https://openreview.net/pdf?id=IMiGRqltQQ}
  }
  

• CamoPatch: An Evolutionary Strategy for Generating Camoflauged Adversarial Patches
  Phoenix Williams+, Ke Li
  Proc. of the 37th Conference on Neural Information Processing Systems (NeurIPS'23)
  Abs | PDF | BiB | ≈ 26.1%

• Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators
  Heng Yang+, Ke Li
  Proc. of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP'23)
  Abs | PDF | BiB

  Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.
  

  @inproceedings{YangL23a,
      author       = {Heng Yang and
                      Ke Li},
      editor       = {Houda Bouamor and
                      Juan Pino and
                      Kalika Bali},
      title        = {InstOptima: Evolutionary Multi-objective Instruction Optimization
          via Large Language Model-based Instruction Operators},
      booktitle    = {EMNLP'23: Findings of the Association for Computational Linguistics: {EMNLP} 2023},
      pages        = {13593--13602},
      publisher    = {Association for Computational Linguistics},
      year         = {2023},
      url          = {https://aclanthology.org/2023.findings-emnlp.907},
      timestamp    = {Wed, 13 Dec 2023 17:20:20 +0100},
      biburl       = {https://dblp.org/rec/conf/emnlp/0008L23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
  Heng Yang+, Ke Li
  Proc. of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), p. 5117–5122, October, 2023.
  10.1145/3583780.3614752
  Abs | PDF | Code | BiB | ≈ 35%

  The advancement of aspect-based sentiment analysis (ABSA) has highlighted the lack of a user-friendly framework that can significantly reduce the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet this demand, we present PyABSA, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to incorporate new models, datasets, and other related tasks. Additionally, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. The project is available at: https://github.com/yangheng95/PyABSA.
  

  @inproceedings{YangZL23,
      author       = {Heng Yang and
                      Chen Zhang and
                      Ke Li},
      title        = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
      booktitle    = {CIKM'23: Proc. of the 32nd {ACM} International Conference on Information and Knowledge Management},
      pages        = {5117--5122},
      publisher    = {{ACM}},
      year         = {2023},
      url          = {https://doi.org/10.1145/3583780.3614752},
      doi          = {10.1145/3583780.3614752},
      timestamp    = {Thu, 23 Nov 2023 13:25:05 +0100},
      biburl       = {https://dblp.org/rec/conf/cikm/0008ZL23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• A Surrogate Assisted Evolutionary Strategy for Image Approximation by Density-Ratio Estimation
  Phoenix Williams+, Ke Li
  Proc. of 2023 IEEE Congress on Evolutionary Computation (CEC'23), p. 1–8, July, 2023.
  Abs | PDF | BiB

  The use of evolutionary strategies in generating images is a common practice within the computational art community. In particular, a popular approach is to approximate a target image using overlapping, semi-transparent shapes and optimizing their attributes to increase similarity to the target image. However, existing methods usually require millions of fitness evaluations to construct good approximations. Within the evolutionary computation and machine learning communities, the use of surrogates has shown to decrease the number of fitness evaluations while achieving state-of-the-art results. Despite the gained traction of surrogate-assisted algorithms, their use within the computational art community is nonexistent. To address this, we extend the previous work of Bayesian Optimization by density-ratio estimation (BORE) to the image approximation task. By estimating the probability of improvement acquisition function using a convolutional probabilistic classifier, we search for solutions that maximize the acquisition function using an evolutionary strategy. By conducting experiments on six different styled target images, we demonstrate the superior performance achieved with the use of surrogate assistance.
  

  @inproceedings{WilliamsLM23,
      author       = {Phoenix Neale Williams and
                      Ke Li and
                      Geyong Min},
      title        = {A Surrogate Assisted Evolutionary Strategy for Image Approximation
          by Density-Ratio Estimation},
      booktitle    = {CEC'23: Proc. of 2023 {IEEE} Congress on Evolutionary Computation},
      pages        = {1--8},
      publisher    = {{IEEE}},
      year         = {2023},
      url          = {https://doi.org/10.1109/CEC53210.2023.10254060},
      doi          = {10.1109/CEC53210.2023.10254060},
      timestamp    = {Fri, 29 Sep 2023 13:35:30 +0200},
      biburl       = {https://dblp.org/rec/conf/cec/WilliamsLM23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems
  Mingyu Huang+, Ke Li
  Porc. of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), p. 5595–5603, August, 2023.
  Abs | PDF | Supp | BiB | ≈ 15%

  One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially share structural similarities with each other. However, due to the black-box nature of combinatorial optimization, it is far from trivial to infer such similarity in real-world scenarios. To bridge this gap, by using local optima network as a proxy of fitness landscapes, this paper proposed to leverage graph data mining techniques to conduct qualitative and quantitative analyses to explore the latent topological structural information embedded in those landscapes. In our experiments, we use the number partitioning problem as the case and our empirical results are inspiring to support the overall assumption of the existence of structural similarity between landscapes within neighboring dimensions. Besides, experiments on simulated annealing demonstrate that the performance of a meta-heuristic solver is similar on structurally similar landscapes.
  

  @inproceedings{HuangL23,
      author       = {Mingyu Huang and
                      Ke Li},
      title        = {Exploring Structural Similarity in Fitness Landscapes via Graph Data
          Mining: {A} Case Study on Number Partitioning Problems},
      booktitle    = {IJCAI'23: Proc. of the Thirty-Second International Joint Conference on Artificial Intelligence},
      pages        = {5595--5603},
      publisher    = {ijcai.org},
      year         = {2023},
      url          = {https://doi.org/10.24963/ijcai.2023/621},
      doi          = {10.24963/IJCAI.2023/621},
      timestamp    = {Mon, 28 Aug 2023 17:23:07 +0200},
      biburl       = {https://dblp.org/rec/conf/ijcai/HuangL23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Boosting Text Augmentation via Hybrid Instance Filtering Framework
  Heng Yang+, Ke Li
  Findings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL'23), p. 1652–1669, July, 2023.
  Abs | PDF | BiB | ≈ 40.6%

  Text augmentation is an effective technique for addressing the problem of insufficient data in natural language processing. However, existing text augmentation methods tend to focus on few-shot scenarios and usually perform poorly on large public datasets. Our research indicates that existing augmentation methods often generate instances with shifted feature spaces, which leads to a drop in performance on the augmented data (for example, EDA generally loses approximately 2% in aspect-based sentiment classification). To address this problem, we propose a hybrid instance-filtering framework (BoostAug) based on pre-trained language models that can maintain a similar feature space with natural datasets. BoostAug is transferable to existing text augmentation methods (such as synonym substitution and back translation) and significantly improves the augmentation performance by 2-3% in classification accuracy. Our experimental results on three classification tasks and nine public datasets show that BoostAug addresses the performance drop problem and outperforms state-of-the-art text augmentation methods. Additionally, we release the code to help improve existing augmentation methods on large datasets.
  

  @inproceedings{YangL23b,
      author       = {Heng Yang and
                      Ke Li},
      editor       = {Anna Rogers and
                      Jordan L. Boyd{-}Graber and
                      Naoaki Okazaki},
      title        = {Boosting Text Augmentation via Hybrid Instance Filtering Framework},
      booktitle    = {ACL'23: Findings of the Association for Computational Linguistics: {ACL} 2023},
      pages        = {1652--1669},
      publisher    = {Association for Computational Linguistics},
      year         = {2023},
      url          = {https://doi.org/10.18653/v1/2023.findings-acl.105},
      doi          = {10.18653/V1/2023.FINDINGS-ACL.105},
      timestamp    = {Sun, 12 Nov 2023 02:14:31 +0100},
      biburl       = {https://dblp.org/rec/conf/acl/0008L23.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Single Application Service Deployment in the Edge Environment Based on the E-CARGO Model
  Senyue Zhang, Ling Xue, Weiliang Huang, Lu Zhao, Ke Li
  Proc. of 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD'23), May, 2023.
  10.1109/CSCWD57460.2023.10152752
  Abs | PDF | BiB

• Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation
  Phoenix Williams+, Ke Li
  Proc. of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'23), p. 12291–12301, June, 2023.
  Abs | PDF | BiB | ≈ 25%

  Deep neural networks (DNNs) are susceptible to adversarial images, raising concerns about their reliability in safety-critical tasks. Sparse adversarial attacks, which limit the number of modified pixels, have shown to be highly effective in causing DNNs to misclassify. However, existing methods often struggle to simultaneously minimize the number of modified pixels and the size of the modifications, often requiring a large number of queries and assuming unrestricted access to the targeted DNN. In contrast, other methods that limit the number of modified pixels often permit unbounded modifications, making them easily detectable. To address these limitations, we propose a novel multi-objective sparse attack algorithm that efficiently minimizes the number of modified pixels and their size during the attack process. Our algorithm draws inspiration from evolutionary computation and incorporates a mechanism for prioritizing objectives that aligns with an attacker's goals. Our approach outperforms existing sparse attacks on CIFAR-10 and ImageNet trained DNN classifiers while requiring only a small query budget, attaining competitive attack success rates while perturbing fewer pixels. Overall, our proposed attack algorithm provides a solution to the limitations of current sparse attack methods by jointly minimizing the number of modified pixels and their size. Our results demonstrate the effectiveness of our approach in restricted scenarios, highlighting its potential to enhance DNN security.
  

  @inproceedings{WilliamsL23,
      author       = {Phoenix Neale Williams and
                      Ke Li},
      title        = {Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation},
      booktitle    = {CVPR'23: Proc. of 2023 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition},
      pages        = {12291--12301},
      publisher    = {{IEEE}},
      year         = {2023},
      url          = {https://doi.org/10.1109/CVPR52729.2023.01183},
      doi          = {10.1109/CVPR52729.2023.01183},
      timestamp    = {Tue, 29 Aug 2023 15:44:40 +0200},
      biburl       = {https://dblp.org/rec/conf/cvpr/Williams023.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
  Renzhi Chen+, Ke Li
  Proc. of the 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO'23), Springer, p. 56-70, March, 2023
  10.1007/978-3-031-27250-9_5
  Abs | PDF | Supp | BiB

  Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on 33 benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.
  

• Sparse Adversarial Attack via Bi-Objective Optimization
  Phoenix Williams+, Ke Li, Geyong Min
  Proc. of the 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO'23), Springer, p. 118-133, March, 2023
  10.1007/978-3-031-27250-9_9
  Abs | PDF | BiB

2022 #

Journal #

• Posterior Decision-Making Based on Decomposition-Driven Knee Point Identification
  Ke Li, Haifeng Nie+, Huiru Gao+, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 26(6): 1409-1423, 2022
  10.1109/TEVC.2021.3116121
  Abs | PDF | Code | Supp | BiB

  Knee points, characterized as a small improvement on one objective can lead to a significant degradation on at least one of the other objectives, are attractive to decision makers in multi-criterion decision-making. This paper presents a simple and effective knee point identification method to help decision makers identify solution(s) of interest from a given set of trade-off solutions thus facilitating posterior decision-making. Our basic idea is to sequentially validate whether a solution is a knee point or not by comparing its localized trade-off utility with others within its neighborhood characterized from a decomposition perspective. In particular, a solution is a knee point if and only if it has the best localized trade-off utility among its neighbors. We implement a GPU version that carries out the knee point identification in a parallel manner. This GPU version reduces the worst-case complexity from quadratic to linear. The performance of our proposed method is compared with five state-of-the-art knee point identification methods on 134 test problem instances and two real-world engineering design problems. Empirical results demonstrate its outstanding performance especially on problems with many local knee points. We further validate the usefulness of our proposed method for guiding evolutionary multi-objective optimization algorithms to search for knee points on the fly during the evolutionary process.
  

  @article{LiNGY22,
      author       = {Ke Li and
                      Haifeng Nie and
                      Huiru Gao and
                      Xin Yao},
      title        = {Posterior Decision Making Based on Decomposition-Driven Knee Point
          Identification},
      journal      = {{IEEE} Trans. Evol. Comput.},
      volume       = {26},
      number       = {6},
      pages        = {1409--1423},
      year         = {2022},
      url          = {https://doi.org/10.1109/TEVC.2021.3116121},
      doi          = {10.1109/TEVC.2021.3116121},
      timestamp    = {Sun, 25 Dec 2022 14:03:21 +0100},
      biburl       = {https://dblp.org/rec/journals/tec/LiNGY22.bib},
      bibsource    = {dblp computer science bibliography, https://dblp.org}
  }
  

• Distributed UAV Swarm Formation and Collision Avoidance Strategies Over Fixed and Switching Topologies
  Jia Wu, Chunbo Luo, Yang Luo, Ke Li
  IEEE Trans. Cybernetics (TCYB), 52(10): 10969-10979, 2022
  10.1109/TCYB.2021.3132587
  Abs | PDF | BiB

  This article proposes a controlling framework for multiple unmanned aerial vehicles (UAVs) to integrate the modes of formation flight and swarm deployment over fixed and switching topologies. Formation strategies enable UAVs to enjoy key collective benefits including reduced energy consumption, but the shape of the formation and each UAV's freedom are significantly restrained. Swarm strategies are thus proposed to maximize each UAV's freedom following simple yet powerful rules. This article investigates the integration and switch between these two strategies, considering the deployment environment factors, such as poor network conditions and unknown and often highly mobile obstacles. We design a distributed formation controller to guide multiple UAVs in orderless states to swiftly reach an intended formation. Inspired by starling birds and similar biological creatures, a distributed collision avoidance controller is proposed to avoid unknown and mobile obstacles. We further illustrated the stability of the controllers over both fixed and switching topologies. The experimental results confirm the effectiveness of the framework.
  

  @article{WuLLL22,
      author    = {Jia Wu and
                   Chunbo Luo and
                   Yang Luo and
                   Ke Li},
      title     = {Distributed {UAV} Swarm Formation and Collision Avoidance Strategies
          Over Fixed and Switching Topologies},
      journal   = {{IEEE} Trans. Cybern.},
      volume    = {52},
      number    = {10},
      pages     = {10969--10979},
      year      = {2022},
      url       = {https://doi.org/10.1109/TCYB.2021.3132587},
      doi       = {10.1109/TCYB.2021.3132587},
      timestamp = {Thu, 13 Oct 2022 16:02:10 +0200},
      biburl    = {https://dblp.org/rec/journals/tcyb/WuLLL22.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Transfer Learning Based Parallel Evolutionary Algorithm Framework for Bi-level Optimization
  Lei Chen, Hai-Lin Liu, Kay Chen Tan, Ke Li
  IEEE Trans. Evolutionary Computation (TEVC), 26(1): 115-129, 2022
  10.1109/TEVC.2021.3095313
  Abs | PDF | Code | BiB

  Evolutionary algorithms (EAs) have been recognized as a promising approach for bilevel optimization. However, the population-based characteristic of EAs largely influences their efficiency and effectiveness due to the nested structure of the two levels of optimization problems. In this article, we propose a transfer learning-based parallel EA (TLEA) framework for bilevel optimization. In this framework, the task of optimizing a set of lower level problems parameterized by upper level variables is conducted in a parallel manner. In the meanwhile, a transfer learning strategy is developed to improve the effectiveness of each lower level search (LLS) process. In practice, we implement two versions of the TLEA: the first version uses the covariance matrix adaptation evolutionary strategy and the second version uses the differential evolution as the evolutionary operator in lower level optimization. The experimental studies on two sets of widely used bilevel optimization benchmark problems are conducted, and the performance of the two TLEA implementations is compared to that of four well-established evolutionary bilevel optimization algorithms to verify the effectiveness and efficiency of the proposed algorithm framework.
  

  @article{ChenLTL22,
    author    = {Lei Chen and
                 Hai{-}Lin Liu and
                 Kay Chen Tan and
                 Ke Li},
    title     = {Transfer Learning-Based Parallel Evolutionary Algorithm Framework
                 for Bilevel Optimization},
    journal   = {{IEEE} Trans. Evol. Comput.},
    volume    = {26},
    number    = {1},
    pages     = {115--129},
    year      = {2022},
    url       = {https://doi.org/10.1109/TEVC.2021.3095313},
    doi       = {10.1109/TEVC.2021.3095313},
    timestamp = {Wed, 23 Feb 2022 11:16:38 +0100},
    biburl    = {https://dblp.org/rec/journals/tec/ChenLTL22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Preference based Multi-Objective Reinforcement Learning for Multi-Microgrid System Optimization Problem in Smart Grid
  Jiangjiao Xu+, Ke Li, Mohammad Abusara
  Memetic Computing (MC), 14(2): 225-235, 2022
  10.1007/s12293-022-00357-w
  Abs | PDF | BiB

  Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.
  

  @article{XuLA22,
      author    = {Jiangjiao Xu and
                   Ke Li and
                   Mohammad Abusara},
      title     = {Preference based multi-objective reinforcement learning for multi-microgrid
          system optimization problem in smart grid},
      journal   = {Memetic Comput.},
      volume    = {14},
      number    = {2},
      pages     = {225--235},
      year      = {2022},
      url       = {https://doi.org/10.1007/s12293-022-00357-w},
      doi       = {10.1007/s12293-022-00357-w},
      timestamp = {Thu, 02 Jun 2022 16:43:14 +0200},
      biburl    = {https://dblp.org/rec/journals/memetic/XuLA22.bib},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Surrogate-Assisted Evolutionary Multi-Objective Optimization for Hardware Design Space Exploration
  Renzhi Chen+, Ke Li
  Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022
  Abs | PDF | BiB

  Hardware design space exploration (DSE) aims to find a suitable micro-architecture for the dedicated hardware accelerators. It is a computationally expensive black-box optimization problem with more than one conflicting performance indicator. Surrogate-assisted evolutionary algorithm is a promising framework for expensive multi-objective optimization problems given its surrogate modeling for handling expensive objective functions and population-based characteristics that search for a set of trade-off solutions simultaneously. However, most, if not all, existing studies mainly focus ‘regular’ Pareto-optimal fronts (PFs), whereas the PF is typically irregular in hardware DSE. In the meanwhile, the gradient information of the differentiable surrogate model(s) is beneficial to navigate a more effective exploration of the search space, but it is yet fully exploited. This paper proposes a surrogate-assisted evolutionary multi-objective optimization based on multiple-gradient descent (MGD) for hardware DSE. Empirical results on both synthetic problems with irregular PFs and real-world hardware DSE cases fully demonstrate the effectiveness and outstanding performance of our proposed algorithm.
  

• Are All Training Data Useful? A Empirical Revisit of Subset Selection in Bayesian Optimization
  Peili Mao+, Ke Li
  Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022
  Abs | PDF | BiB

  Bayesian optimization (BO) has been widely recognized as a powerful approach for black-box optimization problems with expensive objective function(s). Gaussian process (GP), which has been widely used for surrogate modeling in BO, is notorious for its cubic computational complexity grows with the increase of the amount of evaluated samples. This can lead to a significantly increased computational time for BO due to its sequential decision-making nature. This paper revisit the simple and effective subset selection methods to pick up a small group of representative data from the entire dataset to carry out the training and inference of GP in the context of BO. Empirical studies demonstrate that subset selection methods not only promote the performance of the vanilla BO but also significantly reduce the computational time for up to 98%.
  

• Imputation and Forecasting for Multi-Output Gaussian Process in Smart Grid
  Jiangjiao Xu+, Ke Li
  Proc. of the NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022
  Abs | PDF | BiB

• Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?
  Shuang Li+, Ke Li, Wei Li
  Proc. of the 17the International Conference on Parallel Problem Solving from Nature (PPSN'22), Springer, p. 124-137, September, 2022
  10.1007/978-3-031-14721-0_9
  PDF | BiB

@inproceedings{LiLL22,
    author    = {Shuang Li and
                 Ke Li and
                 Wei Li},
    title     = {Do We Really Need to Use Constraint Violation in Constrained Evolutionary
        Multi-objective Optimization?},
    booktitle = {PPSN'22: Proc. of 17th International Conference on Parallel Problem Solving from Nature},
    series    = {Lecture Notes in Computer Science},
    volume    = {13399},
    pages     = {124--137},
    publisher = {Springer},
    year      = {2022},
    url       = {https://doi.org/10.1007/978-3-031-14721-0\_9},
    doi       = {10.1007/978-3-031-14721-0\_9},
    timestamp = {Thu, 25 Aug 2022 08:35:33 +0200},
    biburl    = {https://dblp.org/rec/conf/ppsn/LiLL22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

• Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing
  Shasha Zhou+, Ke Li, Geyong Min
  Proc. of the 17the International Conference on Parallel Problem Solving from Nature (PPSN'22), Springer, p. 341-355, September, 2022
  10.1007/978-3-031-14714-2_24
  PDF | BiB

@inproceedings{ZhouLM22_b,
    author    = {Shasha Zhou and
                 Ke Li and
                 Geyong Min},
    title     = {Attention-Based Genetic Algorithm for Adversarial Attack in Natural
        Language Processing},
    booktitle = {PPSN'22: Proc. of 17th International Conference on Parallel Problem Solving from Nature},
    series    = {Lecture Notes in Computer Science},
    volume    = {13398},
    pages     = {341--355},
    publisher = {Springer},
    year      = {2022},
    url       = {https://doi.org/10.1007/978-3-031-14714-2\_24},
    doi       = {10.1007/978-3-031-14714-2\_24},
    timestamp = {Thu, 25 Aug 2022 08:35:33 +0200},
    biburl    = {https://dblp.org/rec/conf/ppsn/ZhouLM22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

• Black-Box Adversarial Attack via Overlapped Shapes
  Phoenix Williams+, Ke Li, Geyong Min
  Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation (GECCO’22), ACM, p. 467-468, July, 2022
  10.1145/3520304.3528934
  PDF | BiB

@inproceedings{Williams0M22,
    author    = {Phoenix Neale Williams and
                 Ke Li and
                 Geyong Min},
    title     = {Black-box adversarial attack via overlapped shapes},
    booktitle = {{GECCO}'22: Proc. of Genetic and Evolutionary Computation Conference, Companion
        Volume},
    pages     = {467--468},
    publisher = {{ACM}},
    year      = {2022},
    url       = {https://doi.org/10.1145/3520304.3528934},
    doi       = {10.1145/3520304.3528934},
    timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/Williams0M22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

• Adversarial Attack Example Generation via Genetic Algorithm: A Preliminary Result
  Shasha Zhou+, Ke Li, Geyong Min
  Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation (GECCO’22), ACM, p. 469-470, July, 2022
  10.1145/3520304.3528981
  PDF | BiB

@inproceedings{ZhouLM22,
    author    = {Shasha Zhou and
                 Ke Li and
                 Geyong Min},
    title     = {Adversarial example generation via genetic algorithm: a preliminary
        result},
    booktitle = {{GECCO}'22: Proc. of Genetic and Evolutionary Computation Conference, Companion
        Volume},
    pages     = {469--470},
    publisher = {{ACM}},
    year      = {2022},
    url       = {https://doi.org/10.1145/3520304.3528981},
    doi       = {10.1145/3520304.3528981},
    timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/Zhou0M22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

• Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities
  Ke Li, Qingfu Zhang
  Proc. of the 24th Annual Conference on Genetic and Evolutionary Computation (GECCO’22), ACM, p. 469-470, July, 2022
  10.1145/3520304.3533629
  PDF | BiB

@inproceedings{LiZ22,
    author    = {Ke Li and
                 Qingfu Zhang},
    title     = {Decomposition multi-objective optimisation: current developments and
        future opportunities},
    booktitle = {{GECCO}'22: Proc. of the Genetic and Evolutionary Computation Conference, Companion
        Volume},
    pages     = {1554--1571},
    publisher = {{ACM}},
    year      = {2022},
    url       = {https://doi.org/10.1145/3520304.3533629},
    doi       = {10.1145/3520304.3533629},
    timestamp = {Mon, 25 Jul 2022 17:04:27 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/LiZ22.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

2021 #

Journal #

• Vertical Distance-based Clonal Selection Mechanism for the Multiobjective Immune Algorithm
  Lingjie Li+, Qiuzhen Lin, Ke Li, Zhong Ming
  Swarm and Evolutionary Computation (SWEVO), 106: 107299, 2021
  10.1016/j.swevo.2021.100886
  PDF | BiB

  @article{LiLLM21,
    author    = {Lingjie Li and
                 Qiuzhen Lin and
                 Ke Li and
                 Zhong Ming},
    title     = {Vertical distance-based clonal selection mechanism for the multiobjective
                 immune algorithm},
    journal   = {Swarm Evol. Comput.},
    volume    = {63},
    pages     = {100886},
    year      = {2021},
    url       = {https://doi.org/10.1016/j.swevo.2021.100886},
    doi       = {10.1016/j.swevo.2021.100886},
    timestamp = {Tue, 15 Jun 2021 09:16:57 +0200},
    biburl    = {https://dblp.org/rec/journals/swevo/LiLLM21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• A Vector Angles-Based Many-Objective Particle Swarm Optimization Algorithm Using Archive
  Lei Yang+, Xin Hu, Ke Li
  Applied Soft Computing (ASOC), 63: 100886, 2021
  10.1016/j.asoc.2021.107299
  PDF | BiB

  @article{YangHL21,
    author    = {Lei Yang and
                 Xin Hu and
                 Ke Li},
    title     = {A vector angles-based many-objective particle swarm optimization algorithm
                 using archive},
    journal   = {Appl. Soft Comput.},
    volume    = {106},
    pages     = {107299},
    year      = {2021},
    url       = {https://doi.org/10.1016/j.asoc.2021.107299},
    doi       = {10.1016/j.asoc.2021.107299},
    timestamp = {Fri, 03 Dec 2021 13:16:58 +0100},
    biburl    = {https://dblp.org/rec/journals/asc/YangHL21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain
  Ran Wang, Suhe Ye, Ke Li, Sam Kwong
  Information Sciences (INS), 554: 256–275, 2021
  10.1016/j.ins.2020.12.010
  PDF | BiB

  @article{WangYLK21,
    author    = {Ran Wang and
                 Suhe Ye and
                 Ke Li and
                 Sam Kwong},
    title     = {Bayesian network based label correlation analysis for multi-label
                 classifier chain},
    journal   = {Inf. Sci.},
    volume    = {554},
    pages     = {256--275},
    year      = {2021},
    url       = {https://doi.org/10.1016/j.ins.2020.12.010},
    doi       = {10.1016/j.ins.2020.12.010},
    timestamp = {Fri, 09 Apr 2021 18:25:48 +0200},
    biburl    = {https://dblp.org/rec/journals/isci/WangYLK21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

Conference #

• Knee Point Identification Based on the Geometric Characteristic
  Renzhi Chen+, Ke Li
  Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC'21), IEEE, p. 764–769, October, 2021
  10.1109/SMC52423.2021.9658848
  PDF | BiB

  @inproceedings{Chen021,
    author    = {Renzhi Chen and
                 Ke Li},
    title     = {Knee Point Identification Based on the Geometric Characteristic},
    booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 {SMC} 2021, Melbourne, Australia, October 17-20, 2021},
    pages     = {764--769},
    publisher = {{IEEE}},
    year      = {2021},
    url       = {https://doi.org/10.1109/SMC52423.2021.9658848},
    doi       = {10.1109/SMC52423.2021.9658848},
    timestamp = {Tue, 11 Jan 2022 10:00:39 +0100},
    biburl    = {https://dblp.org/rec/conf/smc/Chen021.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Large-Scale Evolutionary Optimization via Multi-Task Random Grouping
  Phoenix Williams+, Ke Li
  Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC'21), IEEE, p. 778–783, October, 2021
  10.1109/SMC52423.2021.9659276
  PDF | BiB

  @inproceedings{WilliamsLM21,
    author    = {Phoenix Neale Williams and
                 Ke Li and
                 Geyong Min},
    title     = {Large-Scale Evolutionary Optimization via Multi-Task Random Grouping},
    booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 {SMC} 2021, Melbourne, Australia, October 17-20, 2021},
    pages     = {778--783},
    publisher = {{IEEE}},
    year      = {2021},
    url       = {https://doi.org/10.1109/SMC52423.2021.9659276},
    doi       = {10.1109/SMC52423.2021.9659276},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/conf/smc/WilliamsLM21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Transfer Bayesian Optimization for Expensive Black-Box Optimization in Dynamic Environment
  Renzhi Chen+, Ke Li
  Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC'21), IEEE, p. 1374–1379, October, 2021
  10.1109/SMC52423.2021.9659200
  PDF | BiB

  @inproceedings{ChenL21,
    author    = {Renzhi Chen and
                 Ke Li},
    title     = {Transfer Bayesian Optimization for Expensive Black-Box Optimization
                 in Dynamic Environment},
    booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 {SMC} 2021, Melbourne, Australia, October 17-20, 2021},
    pages     = {1374--1379},
    publisher = {{IEEE}},
    year      = {2021},
    url       = {https://doi.org/10.1109/SMC52423.2021.9659200},
    doi       = {10.1109/SMC52423.2021.9659200},
    timestamp = {Tue, 11 Jan 2022 10:00:39 +0100},
    biburl    = {https://dblp.org/rec/conf/smc/Chen021a.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• ADMM-based OPF Problem Against Cyber Attacks in Smart Grid
  Jiangjiao Xu+, Ke Li, Mohammad Abusara, Yan Zhang
  Proc. of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC'21), IEEE, p. 1418–1423, October, 2021
  10.1109/SMC52423.2021.9658699
  PDF | BiB

  @inproceedings{XuLA021,
    author    = {Jiangjiao Xu and
                 Ke Li and
                 Mohammad Abusara and
                 Yan Zhang},
    title     = {ADMM-based {OPF} Problem Against Cyber Attacks in Smart Grid},
    booktitle = {2021 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 {SMC} 2021, Melbourne, Australia, October 17-20, 2021},
    pages     = {1418--1423},
    publisher = {{IEEE}},
    year      = {2021},
    url       = {https://doi.org/10.1109/SMC52423.2021.9658699},
    doi       = {10.1109/SMC52423.2021.9658699},
    timestamp = {Thu, 10 Mar 2022 11:05:49 +0100},
    biburl    = {https://dblp.org/rec/conf/smc/XuLA021.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• An Enhancement of the NSGA-II Reliability Optimization using Extended Kalman Filter Based Initialization
  Savas Yuec+, Ke Li
  Proc. of the 2021 20th UK Workshop on Computational Intelligence (UKCI'21), Springer, p. 121–128, September, 2021
  10.1007/978-3-030-87094-2_11
  PDF | BiB

  @inproceedings{YuceL21,
    author    = {Savas Yuce and
                 Ke Li},
    title     = {An Enhancement of the {NSGA-II} Reliability Optimization Using Extended
                 Kalman Filter Based Initialization},
    booktitle = {Advances in Computational Intelligence Systems - Contributions Presented
                 at the 20th {UK} Workshop on Computational Intelligence, September
                 8-10, 2021, Aberystwyth, Wales, {UK}},
    series    = {Advances in Intelligent Systems and Computing},
    volume    = {1409},
    pages     = {121--128},
    publisher = {Springer},
    year      = {2021},
    url       = {https://doi.org/10.1007/978-3-030-87094-2\_11},
    doi       = {10.1007/978-3-030-87094-2\_11},
    timestamp = {Thu, 16 Dec 2021 15:07:27 +0100},
    biburl    = {https://dblp.org/rec/conf/ukci/YuceL21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Empirical Studies on the Role of the Decision Maker in Interactive Evolutionary Multi-Objective Optimization
  Guiyu Lai+, Minhui Liao, Ke Li
  Proc. of the 2021 IEEE Congress on Evolutionary Computation (CEC'21), IEEE, p. 1–8, June, 2021
  10.1109/CEC45853.2021.9504980
  PDF | BiB

  @inproceedings{LaiL021,
    author    = {Guiyu Lai and
                 Minhui Liao and
                 Ke Li},
    title     = {Empirical Studies on the Role of the Decision Maker in Interactive
                 Evolutionary Multi-Objective Optimization},
    booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2021, Krak{\'{o}}w,
                 Poland, June 28 - July 1, 2021},
    pages     = {185--192},
    publisher = {{IEEE}},
    year      = {2021},
    url       = {https://doi.org/10.1109/CEC45853.2021.9504980},
    doi       = {10.1109/CEC45853.2021.9504980},
    timestamp = {Thu, 12 Aug 2021 16:39:59 +0200},
    biburl    = {https://dblp.org/rec/conf/cec/LaiL021.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Empirical Study of Correlations in the Fitness Landscapes of Combinatorial Optimization Problems
  Longfei Zhang+, Ke Li, Shi Gu
  Proc. of the 23th Annual Conference on Genetic and Evolutionary Computation (GECCO’21), ACM, p. 247–248, July, 2021.
  10.1145/3449726.3459528
  PDF | BiB

  @inproceedings{ZhangLG21,
    author    = {Longfei Zhang and
                 Ke Li and
                 Shi Gu},
    title     = {Empirical study of correlations in the fitness landscapes of combinatorial
                 optimization problems},
    booktitle = {{GECCO} '21: Genetic and Evolutionary Computation Conference, Companion
                 Volume, Lille, France, July 10-14, 2021},
    pages     = {247--248},
    publisher = {{ACM}},
    year      = {2021},
    url       = {https://doi.org/10.1145/3449726.3459528},
    doi       = {10.1145/3449726.3459528},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/ZhangLG21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
  Xinyu Shan+, Ke Li
  Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO'21), Springer LNCS, volume 12654, p. 235–247, March, 2021
  10.1007/978-3-030-72062-9_19
  PDF | BiB

  @inproceedings{ShanL21,
    author    = {Xinyu Shan and
                 Ke Li},
    title     = {An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-objective
                 Optimization},
    booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
                 {EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {12654},
    pages     = {235--247},
    publisher = {Springer},
    year      = {2021},
    url       = {https://doi.org/10.1007/978-3-030-72062-9\_19},
    doi       = {10.1007/978-3-030-72062-9\_19},
    timestamp = {Thu, 08 Apr 2021 15:51:58 +0200},
    biburl    = {https://dblp.org/rec/conf/emo/ShanL21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem
  Jiangjiao Xu+, Ke Li, Mohammad Abusara
  Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO'21), Springer LNCS, volume 12654, p. 684–696, March, 2021
  10.1007/978-3-030-72062-9_54
  PDF | BiB

  @inproceedings{XuLA21,
    author    = {Jiangjiao Xu and
                 Ke Li and
                 Mohammad Abusara},
    title     = {Multi-objective Reinforcement Learning Based Multi-microgrid System
                 Optimisation Problem},
    booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
                 {EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {12654},
    pages     = {684--696},
    publisher = {Springer},
    year      = {2021},
    url       = {https://doi.org/10.1007/978-3-030-72062-9\_54},
    doi       = {10.1007/978-3-030-72062-9\_54},
    timestamp = {Mon, 12 Apr 2021 14:42:37 +0200},
    biburl    = {https://dblp.org/rec/conf/emo/XuLA21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Parallel Algorithms for Multiobjective Virtual Network Function Placement Problem
  Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
  Proc. of the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO'21), Springer LNCS, volume 12654, p. 708–720, March, 2021
  10.1007/978-3-030-72062-9_56
  PDF | BiB

  @inproceedings{BillingsleyLMMG21,
    author    = {Joseph Billingsley and
                 Ke Li and
                 Wang Miao and
                 Geyong Min and
                 Nektarios Georgalas},
    title     = {Parallel Algorithms for the Multiobjective Virtual Network Function
                 Placement Problem},
    booktitle = {Evolutionary Multi-Criterion Optimization - 11th International Conference,
                 {EMO} 2021, Shenzhen, China, March 28-31, 2021, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {12654},
    pages     = {708--720},
    publisher = {Springer},
    year      = {2021},EDITOR
    url       = {https://doi.org/10.1007/978-3-030-72062-9\_56},
    doi       = {10.1007/978-3-030-72062-9\_56},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/conf/emo/BillingsleyLMMG21.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2020 #

Journal #

• Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Points
  Ke Li, Minhui Liao+, Kalyanmoy Deb, Geyong Min, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 24(6): 1078–1096, 2020.
  10.1109/TEVC.2020.2987559
  PDF | Code | BiB

  @article{LiLDMY20,
    author    = {Ke Li and
                 Minhui Liao and
                 Kalyanmoy Deb and
                 Geyong Min and
                 Xin Yao},
    title     = {Does Preference Always Help? {A} Holistic Study on Preference-Based
                 Evolutionary Multiobjective Optimization Using Reference Points},
    journal   = {{IEEE} Trans. Evol. Comput.},
    volume    = {24},
    number    = {6},
    pages     = {1078--1096},
    year      = {2020},
    url       = {https://doi.org/10.1109/TEVC.2020.2987559},
    doi       = {10.1109/TEVC.2020.2987559},
    timestamp = {Thu, 17 Dec 2020 18:29:03 +0100},
    biburl    = {https://dblp.org/rec/journals/tec/LiLDMY20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms
  Rong Tang, Ke Li, Wei Ding, Yuntao Wang, Huicheng Zhou, Guangtao Fu
  Water Resources Management, 34: 1005–1020, 2020.
  10.1007/s11269-020-02485-9
  PDF | BiB

  @article{tangLDWZF20,
    title     = {Reference point based multi-objective optimization of reservoir operation: a comparison of three algorithms},
    author    = {Tang, Rong and 
                 Li, Ke and 
                 Ding, Wei and 
                 Wang, Yuntao and 
                 Zhou, Huicheng and 
                 Fu, Guangtao},
    journal   = {Water Resources Management},
    volume    = {34},
    number    = {3},
    pages     = {1005--1020},
    year      = {2020},
    publisher = {Springer}
  }

• Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
  Mengyuan Wu+, Ke Li, Sam Kwong, Qingfu Zhang
  IEEE Trans. Cybernetics (TCYB), 50(2): 753–764, 2020.
  10.1109/TCYB.2018.2872803
  PDF | Supp | Code | BiB

  @article{WuLKZ20,
    author    = {Mengyuan Wu and
                 Ke Li and
                 Sam Kwong and
                 Qingfu Zhang},
    title     = {Evolutionary Many-Objective Optimization Based on Adversarial Decomposition},
    journal   = {{IEEE} Trans. Cybern.},
    volume    = {50},
    number    = {2},
    pages     = {753--764},
    year      = {2020},
    url       = {https://doi.org/10.1109/TCYB.2018.2872803},
    doi       = {10.1109/TCYB.2018.2872803},
    timestamp = {Sat, 30 May 2020 19:51:36 +0200},
    biburl    = {https://dblp.org/rec/journals/tcyb/WuLKZ20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

Conference #

• Performance Analysis of SDN and NFV enabled Mobile Cloud Computing
  Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
  Proc. of the 2020 IEEE Global Communications Conference (GLOBECOM'20), IEEE Press: p. 1–6, December, 2020
  10.1109/GLOBECOM42002.2020.9322530
  PDF | BiB

  @inproceedings{BillingsleyMLMG20,
    author    = {Joseph Billingsley and
                 Wang Miao and
                 Ke Li and
                 Geyong Min and
                 Nektarios Georgalas},
    title     = {Performance Analysis of {SDN} and {NFV} enabled Mobile Cloud Computing},
    booktitle = {{IEEE} Global Communications Conference, {GLOBECOM} 2020, Virtual
                 Event, Taiwan, December 7-11, 2020},
    pages     = {1--6},
    publisher = {{IEEE}},
    year      = {2020},
    url       = {https://doi.org/10.1109/GLOBECOM42002.2020.9322530},
    doi       = {10.1109/GLOBECOM42002.2020.9322530},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/conf/globecom/BillingsleyMLMG20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Knee Point Identification Based on Voronoi Diagram
  Haifeng Nie+, Huiru Gao+, Ke Li
  Proc. of the 2020 IEEE Conference on Systems, Man and Cybernetics (SMC'20), IEEE Press: p. 1–6, December, 2020
  10.1109/SMC42975.2020.9283262
  PDF | BiB

  @inproceedings{NieGL20,
    author    = {Haifeng Nie and
                 Huiru Gao and
                 Ke Li},
    title     = {Knee Point Identification Based on Voronoi Diagram},
    booktitle = {2020 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 {SMC} 2020, Toronto, ON, Canada, October 11-14, 2020},
    pages     = {1081--1086},
    publisher = {{IEEE}},
    year      = {2020},
    url       = {https://doi.org/10.1109/SMC42975.2020.9283262},
    doi       = {10.1109/SMC42975.2020.9283262},
    timestamp = {Fri, 08 Jan 2021 11:20:37 +0100},
    biburl    = {https://dblp.org/rec/conf/smc/NieGL20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction
  Ke Li, Zilin Xiang+, Tao Chen, Kay Chen Tan
  Proc. of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE'20), IEEE Press: September, 2020
  10.1145/3324884.3416617
  PDF | Code | BiB

  @inproceedings{LiXCT20,
    author    = {Ke Li and
                 Zilin Xiang and
                 Tao Chen and
                 Kay Chen Tan},
    title     = {BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project
                 Defect Prediction},
    booktitle = {35th {IEEE/ACM} International Conference on Automated Software Engineering,
                 {ASE} 2020, Melbourne, Australia, September 21-25, 2020},
    pages     = {573--584},
    publisher = {{IEEE}},
    year      = {2020},
    url       = {https://doi.org/10.1145/3324884.3416617},
    doi       = {10.1145/3324884.3416617},
    timestamp = {Fri, 12 Feb 2021 13:04:43 +0100},
    biburl    = {https://dblp.org/rec/conf/kbse/LiXCT20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D
  Lei Sun+, Ke Li
  Proc. of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), Springer LNCS, volume 12270, p. 271–284, Septermber, 2020.
  10.1007/978-3-030-58115-2_19
  PDF | BiB

  @inproceedings{SunL20,
    author    = {Lei Sun and
                 Ke Li},
    title     = {Adaptive Operator Selection Based on Dynamic Thompson Sampling for
                 {MOEA/D}},
    booktitle = {Parallel Problem Solving from Nature - {PPSN} {XVI} - 16th International
                 Conference, {PPSN} 2020, Leiden, The Netherlands, September 5-9, 2020,
                 Proceedings, Part {II}},
    series    = {Lecture Notes in Computer Science},
    volume    = {12270},
    pages     = {271--284},
    publisher = {Springer},
    year      = {2020},
    url       = {https://doi.org/10.1007/978-3-030-58115-2\_19},
    doi       = {10.1007/978-3-030-58115-2\_19},
    timestamp = {Sat, 19 Sep 2020 13:19:33 +0200},
    biburl    = {https://dblp.org/rec/conf/ppsn/SunL20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Surrogate Assisted Evolutionary Algorithm for Medium Scale Multi-Objective Optimisation Problems
  Xiaoran Ruan+, Ke Li, Bilel Derbel, Arnaud Liefooghe
  Proc. of the 22th Annual Conference on Genetic and Evolutionary Computation (GECCO’20), ACM Press: p. 560–568, July, 2020.
  10.1145/3377930.3390191
  PDF | BiB

  @inproceedings{RuanLDL20,
    author    = {Xiaoran Ruan and
                 Ke Li and
                 Bilel Derbel and
                 Arnaud Liefooghe},
    title     = {Surrogate assisted evolutionary algorithm for medium scale multi-objective
                 optimisation problems},
    booktitle = {{GECCO} '20: Genetic and Evolutionary Computation Conference, Canc{\'{u}}n
                 Mexico, July 8-12, 2020},
    pages     = {560--568},
    publisher = {{ACM}},
    year      = {2020},
    url       = {https://doi.org/10.1145/3377930.3390191},
    doi       = {10.1145/3377930.3390191},
    timestamp = {Thu, 19 May 2022 17:23:34 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/RuanLDL20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Routing-Led Placement of VNFs in Arbitrary Networks
  Joseph Billingsley+, Ke Li, Wang Miao, Geyong Min, Nektarios Georgalas
  Proc. of the 2020 World Congress on Computational Intelligence (WCCI'20), IEEE Press: p. 1–8, July, 2020.
  10.1109/CEC48606.2020.9185531
  PDF | BiB

  @article{BillingsleyLMMG20,
    author    = {Joseph Billingsley and
                 Ke Li and
                 Wang Miao and
                 Geyong Min and
                 Nektarios Georgalas},
    title     = {Routing-Led Placement of VNFs in Arbitrary Networks},
    journal   = {CoRR},
    volume    = {abs/2001.11565},
    year      = {2020},
    url       = {https://arxiv.org/abs/2001.11565},
    eprinttype = {arXiv},
    eprint    = {2001.11565},
    timestamp = {Mon, 03 Feb 2020 11:21:05 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2001-11565.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for Dynamic Expensive Multi-Objective Optimisation Problems
  Xuezhou Fan, Ke Li, Kay Chen Tan
  Proc. of the 2020 World Congress on Computational Intelligence (WCCI'20), IEEE Press: p. 1–8, July, 2020.
  10.1109/CEC48606.2020.9185522
  PDF | BiB

  @inproceedings{FanLT20,
    author    = {Xuezhou Fan and
                 Ke Li and
                 Kay Chen Tan},
    title     = {Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning
                 for Dynamic Expensive Multi-Objective Optimisation Problems},
    booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2020, Glasgow,
                 United Kingdom, July 19-24, 2020},
    pages     = {1--8},
    publisher = {{IEEE}},
    year      = {2020},
    url       = {https://doi.org/10.1109/CEC48606.2020.9185522},
    doi       = {10.1109/CEC48606.2020.9185522},
    timestamp = {Fri, 11 Sep 2020 15:04:41 +0200},
    biburl    = {https://dblp.org/rec/conf/cec/FanLT20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• DeepSQLi: Deep Semantic Learning for Testing SQL Injection
  Muyang Liu+, Ke Li, Tao Chen
  Proc. of the ACM SIGSOFT 2020 International Symposium on Software Testing and Analysis (ISSTA'20), ACM Press: p. 286–297, July, 2020.
  10.1145/3395363.3397375
  PDF | Code | BiB

  @inproceedings{LiuLC20,
    author    = {Muyang Liu and
                 Ke Li and
                 Tao Chen},
    title     = {DeepSQLi: deep semantic learning for testing {SQL} injection},
    booktitle = {{ISSTA} '20: 29th {ACM} {SIGSOFT} International Symposium on Software
                 Testing and Analysis, Virtual Event, USA, July 18-22, 2020},
    pages     = {286--297},
    publisher = {{ACM}},
    year      = {2020},
    url       = {https://doi.org/10.1145/3395363.3397375},
    doi       = {10.1145/3395363.3397375},
    timestamp = {Wed, 15 Jul 2020 16:06:56 +0200},
    biburl    = {https://dblp.org/rec/conf/issta/Liu0020.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study
  Ke Li*, Zilin Xiang+*, Tao Chen*, Shuo Wang, Kay Chen Tan
  Proc. of the 42nd International Conference on Software Engineering (ICSE'20), ACM Press: p. 566–577, June, 2020.
  10.1145/3377811.3380360
  PDF | Code | BiB

  @inproceedings{LiX0WT20,
    author    = {Ke Li and
                 Zilin Xiang and
                 Tao Chen and
                 Shuo Wang and
                 Kay Chen Tan},
    title     = {Understanding the automated parameter optimization on transfer learning
                 for cross-project defect prediction: an empirical study},
    booktitle = {{ICSE} '20: 42nd International Conference on Software Engineering,
                 Seoul, South Korea, 27 June - 19 July, 2020},
    pages     = {566--577},
    publisher = {{ACM}},
    year      = {2020},
    url       = {https://doi.org/10.1145/3377811.3380360},
    doi       = {10.1145/3377811.3380360},
    timestamp = {Tue, 12 Jan 2021 14:44:41 +0100},
    biburl    = {https://dblp.org/rec/conf/icse/LiX0WT20.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
  Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang
  Proc. of the 20th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP'20), Springer LNCS, volume 12102, p. 131–147, April, 2020.
  10.1007/978-3-030-43680-3_9
  PDF | BiB

  @inproceedings{PruvostDLL020,
    author    = {Geoffrey Pruvost and
                 Bilel Derbel and
                 Arnaud Liefooghe and
                 Ke Li and
                 Qingfu Zhang},
    title     = {On the Combined Impact of Population Size and Sub-problem Selection
                 in {MOEA/D}},
    booktitle = {Evolutionary Computation in Combinatorial Optimization - 20th European
                 Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain,
                 April 15-17, 2020, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {12102},
    pages     = {131--147},
    publisher = {Springer},
    year      = {2020},
    url       = {https://doi.org/10.1007/978-3-030-43680-3\_9},
    doi       = {10.1007/978-3-030-43680-3\_9},
    timestamp = {Sun, 25 Jul 2021 11:53:08 +0200},
    biburl    = {https://dblp.org/rec/conf/evoW/PruvostDLL020.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2019 #

Journal #

• Learning to Decompose: A Paradigm for Decomposition-Based Multi-Objective Optimization
  Mengyuan Wu+, Ke Li, Sam Kwong, Qingfu Zhang, Jun Zhang
  IEEE Trans. Evolutionary Computation (TEVC), 23(3): 376–390, 2019.
  10.1109/TEVC.2018.2865931
  PDF | Supp | Code | BiB

  @article{WuLKZZ19,
    author    = {Mengyuan Wu and
                 Ke Li and
                 Sam Kwong and
                 Qingfu Zhang and
                 Jun Zhang},
    title     = {Learning to Decompose: {A} Paradigm for Decomposition-Based Multiobjective
                 Optimization},
    journal   = {{IEEE} Trans. Evol. Comput.},
    volume    = {23},
    number    = {3},
    pages     = {376--390},
    year      = {2019},
    url       = {https://doi.org/10.1109/TEVC.2018.2865931},
    doi       = {10.1109/TEVC.2018.2865931},
    timestamp = {Tue, 12 May 2020 16:51:10 +0200},
    biburl    = {https://dblp.org/rec/journals/tec/WuLKZZ19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
  Ke Li*, Renzhi Chen*+, Dragan Savic, Xin Yao
  IEEE Trans. Fuzzy Systems (TFS), 27(5): 845–860, 2019.
  10.1109/TFUZZ.2018.2880700
  PDF | Supp | Code | BiB

  @article{LiCSY19,
    author    = {Ke Li and
                 Renzhi Chen and
                 Dragan A. Savic and
                 Xin Yao},
    title     = {Interactive Decomposition Multiobjective Optimization Via Progressively
                 Learned Value Functions},
    journal   = {{IEEE} Trans. Fuzzy Syst.},
    volume    = {27},
    number    = {5},
    pages     = {849--860},
    year      = {2019},
    url       = {https://doi.org/10.1109/TFUZZ.2018.2880700},
    doi       = {10.1109/TFUZZ.2018.2880700},
    timestamp = {Tue, 12 May 2020 16:52:42 +0200},
    biburl    = {https://dblp.org/rec/journals/tfs/LiCSY19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
  Ke Li*, Renzhi Chen*+, Guangtao Fu, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 23(2): 303–315, 2019.
  10.1109/TEVC.2018.2855411
  PDF | Supp | Code | BiB

  @article{LiCFY19,
    author    = {Ke Li and
                 Renzhi Chen and
                 Guangtao Fu and
                 Xin Yao},
    title     = {Two-Archive Evolutionary Algorithm for Constrained Multiobjective
                 Optimization},
    journal   = {{IEEE} Trans. Evol. Comput.},
    volume    = {23},
    number    = {2},
    pages     = {303--315},
    year      = {2019},
    url       = {https://doi.org/10.1109/TEVC.2018.2855411},
    doi       = {10.1109/TEVC.2018.2855411},
    timestamp = {Tue, 12 May 2020 16:51:04 +0200},
    biburl    = {https://dblp.org/rec/journals/tec/LiCFY19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• A Knee-Point-Based Evolutionary Algorithm Using Weighted Subpopulation for Many-Objective Optimization
  Juan Zou, Chunhui Ji, Shengxiang Yang, Yuping Zhang, Jinhua Zheng, Ke Li
  Swarm and Evolutionary Computation, 47: 33–43, 2019.
  10.1016/j.swevo.2019.02.001
  PDF | BiB

  @article{ZouJYZZL19,
      author    = {Juan Zou and
                   Chunhui Ji and
                   Shengxiang Yang and
                   Yuping Zhang and
                   Jinhua Zheng and
                   Ke Li},
      title     = {A Knee-Point-Based Evolutionary Algorithm Using Weighted Subpopulation for Many-Objective Optimization}, 
      journal   = {Swarm and Evolutionary Computation},
      year      = {2019},
      note      = {accepted for publication}
  }
  

Conference #

• Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution
  Ke Li, Zilin Xiang+, Kay Chen Tan
  Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC'19), IEEE Press: p. 1988–1995, June, 2019.
  10.1109/CEC.2019.8789984
  PDF | Supp | BiB

  @article{LiXT19,
    author    = {Ke Li and
                 Zilin Xiang and
                 Kay Chen Tan},
    title     = {Which Surrogate Works for Empirical Performance Modelling? {A} Case
                 Study with Differential Evolution},
    journal   = {CoRR},
    volume    = {abs/1901.11120},
    year      = {2019},
    url       = {http://arxiv.org/abs/1901.11120},
    eprinttype = {arXiv},
    eprint    = {1901.11120},
    timestamp = {Mon, 16 Mar 2020 17:55:51 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-1901-11120.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons
  Huiru Gao+, Haifeng Nie+, Ke Li
  Proc. of the 2019 IEEE Congress on Evolutionary Computation (CEC'19), IEEE Press: p. 1750–1757, June, 2019.
  10.1109/CEC.2019.8790298
  PDF | Supp | BiB

  @article{GaoNL19,
    author    = {Huiru Gao and
                 Haifeng Nie and
                 Ke Li},
    title     = {Visualisation of Pareto Front Approximation: {A} Short Survey and
                 Empirical Comparisons},
    journal   = {CoRR},
    volume    = {abs/1903.01768},
    year      = {2019},
    url       = {http://arxiv.org/abs/1903.01768},
    eprinttype = {arXiv},
    eprint    = {1903.01768},
    timestamp = {Fri, 08 Jan 2021 11:20:34 +0100},
    biburl    = {https://dblp.org/rec/journals/corr/abs-1903-01768.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities
  Ke Li, Qingfu Zhang
  Proc. of the 21th Annual Conference on Genetic and Evolutionary Computation (GECCO’19): ACM Press: p. 1002–1031, July 2019.
  10.1145/3319619.3323369
  Slides | BiB

  @inproceedings{LiZ19,
    author    = {Ke Li and
                 Qingfu Zhang},
    title     = {Decomposition multi-objective optimisation: current developments and
                 future opportunities},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
                 Companion, {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
    pages     = {1002--1031},
    publisher = {{ACM}},
    year      = {2019},
    url       = {https://doi.org/10.1145/3319619.3323369},
    doi       = {10.1145/3319619.3323369},
    timestamp = {Mon, 15 Jul 2019 16:26:46 +0200},
    biburl    = {https://dblp.org/rec/conf/gecco/LiZ19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Security Testing of Web Applications: A Search-Based Approach for Detecting SQL Injection Vulnerabilities
  Muyang Liu+, Ke Li, Tao Chen
  Proc. of the 21th Annual Conference on Genetic and Evolutionary Computation (GECCO’19), ACM Press: p. 417–418, July 2019.
  10.1145/3319619.3322026
  PDF | BiB

  @inproceedings{LiuLC19,
    author    = {Muyang Liu and
                 Ke Li and
                 Tao Chen},
    title     = {Security testing of web applications: a search-based approach for
                 detecting {SQL} injection vulnerabilities},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
                 Companion, {GECCO} 2019, Prague, Czech Republic, July 13-17, 2019},
    pages     = {417--418},
    publisher = {{ACM}},
    year      = {2019},
    url       = {https://doi.org/10.1145/3319619.3322026},
    doi       = {10.1145/3319619.3322026},
    timestamp = {Wed, 08 Jan 2020 08:56:46 +0100},
    biburl    = {https://dblp.org/rec/conf/gecco/LiuLC19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Progressive Preference Learning: Proof-of-Principle Results in MOEA/D
  Ke Li
  Proc. of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO’19), Springer LNCS, volume 11411, p. 631–643, March 2019.
  10.1007/978-3-030-12598-1_50
  PDF | Supp | Code | BiB

  @inproceedings{Li19,
      author    = {Ke Li},
      title     = {Progressive Preference Learning: Proof-of-Principle Results in {MOEA/D}},
      booktitle = {EMO'19: Proc. of the 10th International Conference Evolutionary Multi-Criterion Optimization},
      pages     = {631--643},
      year      = {2019},
      url       = {https://doi.org/10.1007/978-3-030-12598-1\_50},
      doi       = {10.1007/978-3-030-12598-1\_50},
      timestamp = {Thu, 28 Feb 2019 14:53:34 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/emo/Li19},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• A Formal Model for Multi-objective Optimisation of NFV Placement
  Joseph Billingsley, Ke Li, Wang Miao, Geyong Min, N. Georgalas
  Proc. of the 10th International Conference on Evolutionary Multi-Criterion Optimization (EMO’19), Springer LNCS, volume 11411, p. 529–540, March 2019.
  10.1007/978-3-030-12598-1_42
  PDF | BiB

  @inproceedings{BillingsleyLMMG19,
    author    = {Joseph Billingsley and
                 Ke Li and
                 Wang Miao and
                 Geyong Min and
                 Nektarios Georgalas},
    title     = {A Formal Model for Multi-objective Optimisation of Network Function
                 Virtualisation Placement},
    booktitle = {Evolutionary Multi-Criterion Optimization - 10th International Conference,
                 {EMO} 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {11411},
    pages     = {529--540},
    publisher = {Springer},
    year      = {2019},
    url       = {https://doi.org/10.1007/978-3-030-12598-1\_42},
    doi       = {10.1007/978-3-030-12598-1\_42},
    timestamp = {Fri, 26 Feb 2021 09:21:56 +0100},
    biburl    = {https://dblp.org/rec/conf/emo/BillingsleyLMMG19.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2018 #

Journal #

• R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points
  Ke Li, Kalyanmoy Deb, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 22(6): 821–835, 2018.
  10.1109/TEVC.2017.2737781
  PDF | Supp | Code | BiB

  @article{LiDY18,
    author    = {Ke Li and
                 Kalyanmoy Deb and
                 Xin Yao},
    title     = {R-Metric: Evaluating the Performance of Preference-Based Evolutionary
                 Multiobjective Optimization Using Reference Points},
    journal   = {{IEEE} Trans. Evol. Comput.},
    volume    = {22},
    number    = {6},
    pages     = {821--835},
    year      = {2018},
    url       = {https://doi.org/10.1109/TEVC.2017.2737781},
    doi       = {10.1109/TEVC.2017.2737781},
    timestamp = {Tue, 12 May 2020 16:50:45 +0200},
    biburl    = {https://dblp.org/rec/journals/tec/LiDY18.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Integration of Preferences in Decomposition Multiobjective Optimization
  Ke Li*, Renzhi Chen*+, Geyong Min, Xin Yao
  IEEE Trans. Cybernetics (TCYB), 48(12): 3359–3370, 2018.
  10.1109/TCYB.2018.2859363
  PDF | Supp | Code | BiB

  @article{LiCMY18,
      author    = {Ke Li and
                   Renzhi Chen and
                   Geyong Min and
                   Xin Yao},
      title     = {Integration of Preferences in Decomposition Multiobjective Optimization},
      journal   = {{IEEE} Trans. Cybernetics},
      volume    = {48},
      number    = {12},
      pages     = {3359--3370},
      year      = {2018},
      url       = {https://doi.org/10.1109/TCYB.2018.2859363},
      doi       = {10.1109/TCYB.2018.2859363},
      timestamp = {Sun, 23 Dec 2018 17:21:04 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tcyb/LiCMY18},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Evolutionary Multiobjective Optimization-Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection
  Ran Cheng, Miqing Li, Ke Li, Xin Yao
  IEEE Trans. Evolutionary Computation (TEVC), 22(5): 692–706, 2018.
  10.1109/TEVC.2017.2744328
  PDF | BiB

  @article{ChengLLY18,
      author    = {Ran Cheng and
                   Miqing Li and
                   Ke Li and
                   Xin Yao},
      title     = {Evolutionary Multiobjective Optimization-Based Multimodal Optimization:
          Fitness Landscape Approximation and Peak Detection},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {22},
      number    = {5},
      pages     = {692--706},
      year      = {2018},
      url       = {https://doi.org/10.1109/TEVC.2017.2744328},
      doi       = {10.1109/TEVC.2017.2744328},
      timestamp = {Sun, 23 Dec 2018 17:21:04 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tec/ChengLLY18},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• FEMOSAA: Feature Guided and Knee Driven Multi-Objective Optimization for Self-Adaptive Software at Runtime
  Tao Chen, Ke Li, Rami Bahsoon, Xin Yao
  ACM Trans. Software Engineering and Methodology (TOSEM), 27(2): 1–50, 2018.
  10.1145/3204459
  PDF | Code | BiB

  @article{ChenLBY18,
      author    = {Tao Chen and
                   Ke Li and
                   Rami Bahsoon and
                   Xin Yao},
      title     = {{FEMOSAA:} Feature-Guided and Knee-Driven Multi-Objective Optimization
          for Self-Adaptive Software},
      journal   = {{ACM} Trans. Softw. Eng. Methodol.},
      volume    = {27},
      number    = {2},
      pages     = {5:1--5:50},
      year      = {2018},
      url       = {https://doi.org/10.1145/3204459},
      doi       = {10.1145/3204459},
      timestamp = {Wed, 21 Nov 2018 12:44:28 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tosem/ChenLBY18},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Dynamic Multi-Objectives Optimization with a Changing Number of Objectives
  Ke Li*, Renzhi Chen*+, Xin Yao,
  IEEE Trans. Evolutionary Computation (TEVC), 21(1): 157–171, 2018.
  10.1109/TEVC.2017.2669638
  PDF | Supp | Code | BiB

  @article{ChenLY18,
      author    = {Renzhi Chen and
                   Ke Li and
                   Xin Yao},
      title     = {Dynamic Multiobjectives Optimization With a Changing Number of Objectives},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {22},
      number    = {1},
      pages     = {157--171},
      year      = {2018},
      url       = {https://doi.org/10.1109/TEVC.2017.2669638},
      doi       = {10.1109/TEVC.2017.2669638},
      timestamp = {Wed, 04 Jul 2018 13:22:50 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/tec/ChenLY18},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Multi-Tenant Cloud Service Composition using Evolutionary Optimization
  Satish Kumar, Rami Bahsoon, Tao Chen, Ke Li, R. Buyya,
  Proc. of the 24th International Conference on Parallel and Distributed Systems (ICPADS’18), IEEE Press: p. 972–979, December 2018.
  10.1109/PADSW.2018.8644640
  PDF | BiB

  @inproceedings{KumarBCLB18,
      author    = {Satish Kumar and
                   Rami Bahsoon and
                   Tao Chen and
                   Ke Li and
                   Rajkumar Buyya},
      title     = {Multi-Tenant Cloud Service Composition Using Evolutionary Optimization},
      booktitle = {ICPADS: Proc. of the 24th {IEEE} International Conference on Parallel and Distributed Systems},
      pages     = {972--979},
      year      = {2018},
      url       = {https://doi.org/10.1109/PADSW.2018.8644640},
      doi       = {10.1109/PADSW.2018.8644640},
      timestamp = {Tue, 26 Feb 2019 16:36:22 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/icpads/KumarBCLB18},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Decomposition Multi-Objective Optimisation: Current Developments and Future Opportunities
  Ke Li, Qingfu Zhang
  Proc. of the 20th Annual Conference on Genetic and Evolutionary Computation (GECCO’18), ACM Press: p. 903–936, July 2018.
  10.1145/3205651.3207856
  Slides | BiB

@inproceedings{LiZ18,
  author    = {Ke Li and
               Qingfu Zhang},
  title     = {Decomposition multi-objective optimisation: current developments and
               future opportunities},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference
               Companion, {GECCO} 2018, Kyoto, Japan, July 15-19, 2018},
  pages     = {907--936},
  publisher = {{ACM}},
  year      = {2018},
  url       = {https://doi.org/10.1145/3205651.3207856},
  doi       = {10.1145/3205651.3207856},
  timestamp = {Fri, 27 Dec 2019 21:30:09 +0100},
  biburl    = {https://dblp.org/rec/conf/gecco/0001Z18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{LiZ18,
    author    = {Ke Li and
                 Qingfu Zhang},
    title     = {Decomposition multi-objective optimisation: current developments and
        future opportunities},
    booktitle = {GECCO'18: Proc. of the 20th Genetic and Evolutionary Computation Conference
        Companion},
    pages     = {907--936},
    year      = {2018},
    url       = {https://doi.org/10.1145/3205651.3207856},
    doi       = {10.1145/3205651.3207856},
    timestamp = {Wed, 21 Nov 2018 12:43:54 +0100},
    biburl    = {https://dblp.org/rec/bib/conf/gecco/0001Z18},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

2017 #

Journal #

• Efficient Non-domination Level Update Method for Steady-State Evolutionary Multiobjective Optimization
  Ke Li, Kalyanmoy Deb, Qingfu Zhang, Qiang Zhang
  IEEE Trans. Cybernetics (TCYB), 47(9): 2838–2849, 2017.
  10.1109/TCYB.2016.2621008
  PDF | Supp | Code | BiB

  @article{LiDZZ17,
      author    = {Ke Li and
                   Kalyanmoy Deb and
                   Qingfu Zhang and
                   Qiang Zhang},
      title     = {Efficient Nondomination Level Update Method for Steady-State Evolutionary
          Multiobjective Optimization},
      journal   = {{IEEE} Trans. Cybernetics},
      volume    = {47},
      number    = {9},
      pages     = {2838--2849},
      year      = {2017},
      url       = {https://doi.org/10.1109/TCYB.2016.2621008},
      doi       = {10.1109/TCYB.2016.2621008},
      timestamp = {Wed, 14 Nov 2018 10:31:31 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tcyb/LiDZZ17},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
  Mengyuan Wu+, Ke Li, Sam Kwong, Yu Zhou, Qingfu Zhang
  IEEE Trans. Evolutionary Computation (TEVC), 21(4): 554–568, 2017.
  10.1109/TEVC.2017.2656922
  PDF | Supp | Code | BiB

  @article{WuLKZZ17,
      author    = {Mengyuan Wu and
                   Ke Li and
                   Sam Kwong and
                   Yu Zhou and
                   Qingfu Zhang},
      title     = {Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective
          Optimization},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {21},
      number    = {4},
      pages     = {554--568},
      year      = {2017},
      url       = {https://doi.org/10.1109/TEVC.2017.2656922},
      doi       = {10.1109/TEVC.2017.2656922},
      timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/tec/WuLKZZ17},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Recent advances in semantic computing and personalization
  Haoran Xie, Fu Lee Wang, Xudong Mao, Ke Li, Qing Li, Handing Wang
  Neurocomputing (NEUCOM). 254: 1–2, 2017.
  10.1016/j.neucom.2017.02.073
  PDF | BiB

  @article{XieWMLLW17,
      author    = {Haoran Xie and
                   Fu Lee Wang and
                   Xudong Mao and
                   Ke Li and
                   Qing Li and
                   Handing Wang},
      title     = {Recent advances in semantic computing and personalization},
      journal   = {Neurocomputing},
      volume    = {254},
      pages     = {1--2},
      year      = {2017},
      url       = {https://doi.org/10.1016/j.neucom.2017.02.073},
      doi       = {10.1016/j.neucom.2017.02.073},
      timestamp = {Fri, 30 Nov 2018 13:23:21 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/ijon/XieWMLLW17},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Empirical Investigations of Reference Point Based Methods When Facing a Massively Large Number of Objectives: First Results
  Ke Li, Kalyamoy Deb, Tolga Altinoz, Xin Yao
  Proc. of the 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO’17), Springer LNCS, volume 10173, p. 390–405, March 2017.
  10.1007/978-3-319-54157-0_27
  PDF | BiB

  @inproceedings{LiDAY17,
      author    = {Ke Li and
                   Kalyanmoy Deb and
                   Okkes Tolga Altin{\"{o}}z and
                   Xin Yao},
      title     = {Empirical Investigations of Reference Point Based Methods When Facing
          a Massively Large Number of Objectives: First Results},
      booktitle = {EMO'17: Proc. of the 9th International Conference Evolutionary Multi-Criterion Optimization},
          pages     = {390--405},
          year      = {2017},
          url       = {https://doi.org/10.1007/978-3-319-54157-0\_27},
          doi       = {10.1007/978-3-319-54157-0\_27},
          timestamp = {Wed, 14 Nov 2018 10:59:29 +0100},
          biburl    = {https://dblp.org/rec/bib/conf/emo/LiDAY17},
          bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Adaptive weights generation for decomposition-based multi-objective optimization using Gaussian process regression
  Mengyuan Wu, Sam Kwong, Yuheng Jia, Ke Li, Qingfu Zhang
  Proc. of the 19th Annual Conference on Genetic and Evolutionary Computation (GECCO’17), ACM Press: p. 641–648, July 2017.
  10.1145/3071178.3071339
  PDF | BiB

  @inproceedings{WuKJLZ17,
      author    = {Mengyuan Wu and
                   Sam Kwong and
                   Yuheng Jia and
                   Ke Li and
                   Qingfu Zhang},
      title     = {Adaptive weights generation for decomposition-based multi-objective
          optimization using Gaussian process regression},
      booktitle = {GECCO'17: Proc. of the 19th Genetic and Evolutionary Computation Conference},
      pages     = {641--648},
      year      = {2017},
      url       = {https://doi.org/10.1145/3071178.3071339},
      doi       = {10.1145/3071178.3071339},
      timestamp = {Tue, 06 Nov 2018 11:06:40 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/gecco/WuKJLZ17},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

2016 #

Journal #

• Personalized Search for Social Media via Dominating Verbal Context
  Haoran Xie, Xiaodong Li, Tao Wang, Li Chen, Ke Li, Fu Lee Wang, Yi Cai, Qing Li, Huaqing Min
  Neurocomputing (NEUCOM). 172: 27–37, 2016.
  10.1016/j.neucom.2014.12.109
  PDF | BiB

  @article{XieLWCLWCLM16,
      author    = {Haoran Xie and
                   Xiaodong Li and
                   Tao Wang and
                   Li Chen and
                   Ke Li and
                   Fu Lee Wang and
                   Yi Cai and
                   Qing Li and
                   Huaqing Min},
      title     = {Personalized search for social media via dominating verbal context},
      journal   = {Neurocomputing},
      volume    = {172},
      pages     = {27--37},
      year      = {2016},
      url       = {https://doi.org/10.1016/j.neucom.2014.12.109},
      doi       = {10.1016/j.neucom.2014.12.109},
      timestamp = {Wed, 14 Nov 2018 10:26:17 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/ijon/XieLWCLWCLM16},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Variable Interactions in Multi-Objective Optimization Problems
  Ke Li, Mohammad Nabi Omidvar, Kalyanmoy Deb, Xin Yao
  Proc. of the 14th International Conference on Parallel Problem Solving from Nature (PPSN’16), Springer LNCS, volume 9921, p. 399–409, September 2016.
  10.1007/978-3-319-45823-6_37
  PDF | BiB

  @inproceedings{LiODY16,
    author    = {Ke Li and
                 Mohammad Nabi Omidvar and
                 Kalyanmoy Deb and
                 Xin Yao},
    title     = {Variable Interaction in Multi-objective Optimization Problems},
    booktitle = {Parallel Problem Solving from Nature - {PPSN} {XIV} - 14th International
                 Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
    series    = {Lecture Notes in Computer Science},
    volume    = {9921},
    pages     = {399--409},
    publisher = {Springer},
    year      = {2016},
    url       = {https://doi.org/10.1007/978-3-319-45823-6\_37},
    doi       = {10.1007/978-3-319-45823-6\_37},
    timestamp = {Wed, 19 Jan 2022 09:30:47 +0100},
    biburl    = {https://dblp.org/rec/conf/ppsn/LiODY16.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2015 #

Journal #

• An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition
  Ke Li, Kalyanmoy Deb, Qingfu Zhang, Sam Kwong
  IEEE Trans. Evolutionary Computation (TEVC), 19(5): 694–716, 2015.
  10.1109/TEVC.2014.2373386
  PDF | Code | BiB

  @article{LiDZK15,
      author    = {Ke Li and
                   Kalyanmoy Deb and
                   Qingfu Zhang and
                   Sam Kwong},
      title     = {An Evolutionary Many-Objective Optimization Algorithm Based on Dominance
          and Decomposition},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {19},
      number    = {5},
      pages     = {694--716},
      year      = {2015},
      url       = {https://doi.org/10.1109/TEVC.2014.2373386},
      doi       = {10.1109/TEVC.2014.2373386},
      timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/tec/LiDZK15},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Interrelationship-Based Selection for Decomposition Multiobjective Optimization
  Ke Li, Sam Kwong, Qingfu Zhang, Kalyanmoy Deb
  IEEE Trans. Cybernetics (TCYB), 45(10): 2076–2088, 2015
  10.1109/TCYB.2014.2365354
  PDF | PDF | Code | BiB

  @article{LiKZD15,
      author    = {Ke Li and
                   Sam Kwong and
                   Qingfu Zhang and
                   Kalyanmoy Deb},
      title     = {Interrelationship-Based Selection for Decomposition Multiobjective
          Optimization},
      journal   = {{IEEE} Trans. Cybernetics},
      volume    = {45},
      number    = {10},
      pages     = {2076--2088},
      year      = {2015},
      url       = {https://doi.org/10.1109/TCYB.2014.2365354},
      doi       = {10.1109/TCYB.2014.2365354},
      timestamp = {Wed, 14 Nov 2018 10:31:35 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tcyb/LiKZD15},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• A Dual-Population Paradigm for Evolutionary Multiobjective Optimization
  Ke Li, Sam Kwong, Kalyanmoy Deb
  Information Sciences (INS), 309: 50–72, 2015.
  10.1016/j.ins.2015.03.002
  PDF | Code | BiB

  @article{LiKD15,
      author    = {Ke Li and
                   Sam Kwong and
                   Kalyanmoy Deb},
      title     = {A dual-population paradigm for evolutionary multiobjective optimization},
      journal   = {Inf. Sci.},
      volume    = {309},
      pages     = {50--72},
      year      = {2015},
      url       = {https://doi.org/10.1016/j.ins.2015.03.002},
      doi       = {10.1016/j.ins.2015.03.002},
      timestamp = {Wed, 14 Nov 2018 10:27:37 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/isci/LiKD15},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Class-Specific Soft Voting based Multiple Extreme Learning Machines Ensemble
  Jingjing Cao, Sam Kwong, Ran Wang, Xiaodong Li, Ke Li, Xiangfei Kong
  Neurocomputing (NEUCOM). 149: 275–284, 2015.
  10.1016/j.neucom.2014.02.072
  PDF | BiB

  @article{CaoKWLLK15,
      author    = {Jingjing Cao and
                   Sam Kwong and
                   Ran Wang and
                   Xiaodong Li and
                   Ke Li and
                   Xiangfei Kong},
      title     = {Class-specific soft voting based multiple extreme learning machines
          ensemble},
      journal   = {Neurocomputing},
      volume    = {149},
      pages     = {275--284},
      year      = {2015},
      url       = {https://doi.org/10.1016/j.neucom.2014.02.072},
      doi       = {10.1016/j.neucom.2014.02.072},
      timestamp = {Wed, 14 Nov 2018 10:26:17 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/ijon/CaoKWLLK15},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Two-Level Stable Matching-Based Selection in MOEA/D
  Mengyuan Wu, Sam Kwong, Qingfu Zhang, Ke Li, Ran Wang, Bo Liu
  Proc. of 2015 IEEE Conference on Systems, Mans and Cybernetics (SMC’15), IEEE Press: p. 1720–1725, October 2015.
  10.1109/SMC.2015.302
  PDF | Code | BiB

  @inproceedings{WuKZLWL15,
    author    = {Mengyuan Wu and
                 Sam Kwong and
                 Qingfu Zhang and
                 Ke Li and
                 Ran Wang and
                 Bo Liu},
    title     = {Two-Level Stable Matching-Based Selection in {MOEA/D}},
    booktitle = {2015 {IEEE} International Conference on Systems, Man, and Cybernetics,
                 Kowloon Tong, Hong Kong, October 9-12, 2015},
    pages     = {1720--1725},
    publisher = {{IEEE}},
    year      = {2015},
    url       = {https://doi.org/10.1109/SMC.2015.302},
    doi       = {10.1109/SMC.2015.302},
    timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
    biburl    = {https://dblp.org/rec/conf/smc/WuKZLWL15.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

• Evolutionary Multiobjective Optimization with Hybrid Selection Principles
  Ke Li, Kalyanmoy Deb, Qingfu Zhang
  Proc. of 2015 IEEE Congress on Evolutionary Computation (CEC’15), IEEE Press: p. 900–907, May 2015.
  10.1109/CEC.2015.7256986
  PDF | Code | BiB

@inproceedings{LiDZ15,
  author    = {Ke Li and
               Kalyanmoy Deb and
               Qingfu Zhang},
  title     = {Evolutionary multiobjective optimization with hybrid selection principles},
  booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2015, Sendai, Japan,
               May 25-28, 2015},
  pages     = {900--907},
  publisher = {{IEEE}},
  year      = {2015},
  url       = {https://doi.org/10.1109/CEC.2015.7256986},
  doi       = {10.1109/CEC.2015.7256986},
  timestamp = {Wed, 16 Oct 2019 14:14:52 +0200},
  biburl    = {https://dblp.org/rec/conf/cec/LiDZ15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{LiZKLW14,
    author    = {Ke Li and
                 Qingfu Zhang and
                 Sam Kwong and
                 Miqing Li and
                 Ran Wang},
    title     = {Stable Matching-Based Selection in Evolutionary Multiobjective Optimization},
    journal   = {{IEEE} Trans. Evolutionary Computation},
    volume    = {18},
    number    = {6},
    pages     = {909--923},
    year      = {2014},
    url       = {https://doi.org/10.1109/TEVC.2013.2293776},
    doi       = {10.1109/TEVC.2013.2293776},
    timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
    biburl    = {https://dblp.org/rec/bib/journals/tec/LiZKLW14},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

2014 #

Journal #

• Stable Matching Based Selection in Evolutionary Multiobjective Optimization
  Ke Li, Qingfu Zhang, Sam Kwong, Miqing Li, Ran Wang
  IEEE Trans. Evolutionary Computation (TEVC). 18(6): 909–923, 2014.
  10.1109/TEVC.2013.2293776
  PDF | Code | BiB

  @article{LiZKLW14,
      author    = {Ke Li and
                   Qingfu Zhang and
                   Sam Kwong and
                   Miqing Li and
                   Ran Wang},
      title     = {Stable Matching-Based Selection in Evolutionary Multiobjective Optimization},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {18},
      number    = {6},
      pages     = {909--923},
      year      = {2014},
      url       = {https://doi.org/10.1109/TEVC.2013.2293776},
      doi       = {10.1109/TEVC.2013.2293776},
      timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/tec/LiZKLW14},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Adaptive Operator Selection with Bandits for Multiobjective Evolutionary Algorithm Based on Decomposition
  Ke Li, Álvaro Fialho, Sam Kwong, Qingfu Zhang
  IEEE Trans. Evolutionary Computation (TEVC). 18(1): 114–130, 2014.
  10.1109/TEVC.2013.2239648
  PDF | Supp | Code | BiB

  @article{LiFKZ14,
      author    = {Ke Li and
                   {\'{A}}lvaro Fialho and
                   Sam Kwong and
                   Qingfu Zhang},
      title     = {Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary
          Algorithm Based on Decomposition},
      journal   = {{IEEE} Trans. Evolutionary Computation},
      volume    = {18},
      number    = {1},
      pages     = {114--130},
      year      = {2014},
      url       = {https://doi.org/10.1109/TEVC.2013.2239648},
      doi       = {10.1109/TEVC.2013.2239648},
      timestamp = {Sun, 23 Sep 2018 19:44:31 +0200},
      biburl    = {https://dblp.org/rec/bib/journals/tec/LiFKZ14},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• A General Framework for Evolutionary Multiobjective Optimization via Manifold Learning
  Ke Li, Sam Kwong
  Neurocomputing (NEUCOM). 146: 65–74, 2014.
  10.1016/j.neucom.2014.03.070
  PDF | BiB

  @article{LiK14,
      author    = {Ke Li and
                   Sam Kwong},
      title     = {A general framework for evolutionary multiobjective optimization via
          manifold learning},
      journal   = {Neurocomputing},
      volume    = {146},
      pages     = {65--74},
      year      = {2014},
      url       = {https://doi.org/10.1016/j.neucom.2014.03.070},
      doi       = {10.1016/j.neucom.2014.03.070},
      timestamp = {Wed, 14 Nov 2018 10:26:16 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/ijon/LiK14},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Evolutionary Algorithms with Segment-based Search for Multiobjective Optimization Problems
  Miqing Li, Shengxiang Yang, Ke Li, Xiaohui Liu
  IEEE Trans. Cybernetics (TCYB). 44(8): 1295–1313, 2014.
  10.1109/TCYB.2013.2282503
  PDF | BiB

  @article{LiYLL14,
      author    = {Miqing Li and
                   Shengxiang Yang and
                   Ke Li and
                   Xiaohui Liu},
      title     = {Evolutionary Algorithms With Segment-Based Search for Multiobjective
          Optimization Problems},
      journal   = {{IEEE} Trans. Cybernetics},
      volume    = {44},
      number    = {8},
      pages     = {1295--1313},
      year      = {2014},
      url       = {https://doi.org/10.1109/TCYB.2013.2282503},
      doi       = {10.1109/TCYB.2013.2282503},
      timestamp = {Wed, 14 Nov 2018 10:31:34 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/tcyb/LiYLL14},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• An Indicator-based Selection Multi-Objective Evolutionary Algorithm with Preference for Multi-Class Ensemble
  Jingjing Cao, Sam Kwong, Ran Wang, Ke Li
  Proc. of 2014 International Conference on Machine Learning and Cybernetics (ICMLC’12), IEEE Press: p. 147–152, July 2014.
  10.1109/ICMLC.2014.7009108
  PDF | BiB

@inproceedings{CaoKWL14,
    author    = {Jingjing Cao and
                 Sam Kwong and
                 Ran Wang and
                 Ke Li},
    title     = {{AN} indicator-based selection multi-objective evolutionary algorithm
        with preference for multi-class ensemble},
    booktitle = {ICMLC'14: Proc. of the 2014 International Conference on Machine Learning and Cybernetics},
    pages     = {147--152},
    year      = {2014},
    url       = {https://doi.org/10.1109/ICMLC.2014.7009108},
    doi       = {10.1109/ICMLC.2014.7009108},
    timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
    biburl    = {https://dblp.org/rec/bib/conf/icmlc/CaoKWL14},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{LiKWTM13,
    author    = {Ke Li and
                 Sam Kwong and
                 Ran Wang and
                 Kit{-}Sang Tang and
                 Kim{-}Fung Man},
    title     = {Learning paradigm based on jumping genes: {A} general framework for
        enhancing exploration in evolutionary multiobjective optimization},
    journal   = {Inf. Sci.},
    volume    = {226},
    pages     = {1--22},
    year      = {2013},
    url       = {https://doi.org/10.1016/j.ins.2012.11.002},
    doi       = {10.1016/j.ins.2012.11.002},
    timestamp = {Wed, 14 Nov 2018 10:27:36 +0100},
    biburl    = {https://dblp.org/rec/bib/journals/isci/LiKWTM13},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}

2013 #

Journal #

• Learning Paradigm Based on Jumping Genes: A General Framework for Enhancing Exploration in Evolutionary Multiobjective Optimization
  Ke Li, Sam Kwong, Ran Wang, Kit-Sang Tang, Kim-Fung Man
  Information Sciences (INS), 226: 1–22, 2013.
  10.1016/j.ins.2012.11.002
  PDF | Code | BiB

  @article{LiKWTM13,
      author    = {Ke Li and
                   Sam Kwong and
                   Ran Wang and
                   Kit{-}Sang Tang and
                   Kim{-}Fung Man},
      title     = {Learning paradigm based on jumping genes: {A} general framework for
          enhancing exploration in evolutionary multiobjective optimization},
      journal   = {Inf. Sci.},
      volume    = {226},
      pages     = {1--22},
      year      = {2013},
      url       = {https://doi.org/10.1016/j.ins.2012.11.002},
      doi       = {10.1016/j.ins.2012.11.002},
      timestamp = {Wed, 14 Nov 2018 10:27:36 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/isci/LiKWTM13},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Evolving Extreme Learning Machine Paradigm with Adaptive Operator Selection and Parameter Control
  Ke Li, Ran Wang, Sam Kwong, Jingjing Cao
  International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS). 21(supp02): 143–154, 2013.
  10.1142/S0218488513400229
  PDF | Code | BiB

  @ARTICLE{IJUFKS13,
    author = {Ke Li and
              Ran Wang and
              Sam Kwong and
              Jingjing Cao},
    title = {Evolving Extreme Learning Machine Paradigm with Adaptive Operator
  	Selection and Parameter Control},
    journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based
  	Systems},
    year = {2013},
    volume = {21},
    pages = {143-154}
  }

2012 #

Journal #

• Achieving Balance Between Proximity and Diversity in Multi-objective Evolutionary Algorithm
  Ke Li, Sam Kwong, Jingjing Cao, Miqing Li, Jinhua Zheng, Ruimin Shen
  Information Sciences (INS), 182(1): 220–242, 2012.
  10.1016/j.ins.2011.08.027
  PDF | BiB

  @article{LiKCLZS12,
      author    = {Ke Li and
                   Sam Kwong and
                   Jingjing Cao and
                   Miqing Li and
                   Jinhua Zheng and
                   Ruimin Shen},
      title     = {Achieving balance between proximity and diversity in multi-objective
          evolutionary algorithm},
      journal   = {Inf. Sci.},
      volume    = {182},
      number    = {1},
      pages     = {220--242},
      year      = {2012},
      url       = {https://doi.org/10.1016/j.ins.2011.08.027},
      doi       = {10.1016/j.ins.2011.08.027},
      timestamp = {Wed, 14 Nov 2018 10:27:37 +0100},
      biburl    = {https://dblp.org/rec/bib/journals/isci/LiKCLZS12},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

Conference #

• Multi-Objective Differential Evolution with Self-Navigation
  Ke Li, Sam Kwong, Ran Wang, Jingjing Cao, Imre J. Rudas
  Proc. of 2012 IEEE International Conference on Systems, Mans and Cybernetics (SMC’12), IEEE Press: p. 508–513, October 2012.
  10.1109/ICSMC.2012.6377775
  PDF | BiB

  @inproceedings{LiKWCR12,
      author    = {Ke Li and
                   Sam Kwong and
                   Ran Wang and
                   Jingjing Cao and
                   Imre J. Rudas},
      title     = {Multi-objective differential evolution with self-navigation},
      booktitle = {SMC'12: Proc. of the 2012 {IEEE} International Conference on Systems, Man,
          and Cybernetics},
      pages     = {508--513},
      year      = {2012},
      url       = {https://doi.org/10.1109/ICSMC.2012.6377775},
      doi       = {10.1109/ICSMC.2012.6377775},
      timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/smc/LiKWCR12},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• A Weighted Voting Method Using Minimum Square Error based on Extreme Learning Machine
  Jingjing Cao, Sam Kwong, Ran Wang, Ke Li
  Proc. of 2012 International Conference on Machine Learning and Cybernetics (ICMLC’12), IEEE Press: p. 411–414, July 2012.
  10.1109/ICMLC.2012.6358949
  PDF | BiB

  @inproceedings{CaoKWL12,
      author    = {Jingjing Cao and
                   Sam Kwong and
                   Ran Wang and
                   Ke Li},
      title     = {A weighted voting method using minimum square error based on Extreme
          Learning Machine},
      booktitle = {ICMLC'12: Proc. of the 2012 International Conference on Machine Learning and Cybernetics},
      pages     = {411--414},
      year      = {2012},
      url       = {https://doi.org/10.1109/ICMLC.2012.6358949},
      doi       = {10.1109/ICMLC.2012.6358949},
      timestamp = {Mon, 29 Jan 2018 16:21:37 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/icmlc/CaoKWL12},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

2011 #

Conference #

• Combining Interpretable Fuzzy Rule-based Classifiers via Multi-Objective Hierarchical Evolutionary Algorithm
  Jingjing Cao, Hanli Wang, Sam Kwong, Ke Li
  Proc. of 2011 IEEE International Conference on Systems, Mans and Cybernetics (SMC’11), IEEE Press: p. 1771–1776, October 2011.
  10.1109/ICSMC.2011.6083928
  PDF | BiB

  @inproceedings{CaoWKL11,
      author    = {Jingjing Cao and
                   Hanli Wang and
                   Sam Kwong and
                   Ke Li},
      title     = {Combining interpretable fuzzy rule-based classifiers via multi-objective
          hierarchical evolutionary algorithm},
      booktitle = {ICMLC'11: Proc. of the 2011 {IEEE} International Conference on Systems, Man
          and Cybernetics},
      pages     = {1771--1776},
      year      = {2011},
      url       = {https://doi.org/10.1109/ICSMC.2011.6083928},
      doi       = {10.1109/ICSMC.2011.6083928},
      timestamp = {Mon, 27 Nov 2017 16:55:26 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/smc/CaoWKL11},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• JGBL paradigm: A Novel Strategy to Enhance the Exploration Ability of NSGA-II
  Ke Li, Sam Kwong, Kim-Fung Man
  Proc. of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO’11), ACM Press: p. 99–100, July 2011.
  10.1145/2001858.2001915
  PDF | BiB

  @inproceedings{LiKM11,
      author    = {Ke Li and
                   Sam Kwong and
                   Kim{-}Fung Man},
      title     = {{JGBL} paradigm: a novel strategy to enhance the exploration ability
          of nsga-ii},
      booktitle = {GECCO'11: Proc. of the 13th Annual Genetic and Evolutionary Computation Conference},
      pages     = {99--100},
      year      = {2011},
      url       = {https://doi.org/10.1145/2001858.2001915},
      doi       = {10.1145/2001858.2001915},
      timestamp = {Tue, 06 Nov 2018 11:06:35 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/gecco/LiKM11},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Multi-Objective Differential Evolution with Adaptive Control of Parameters and Operators
  Ke Li, Álvaro Fialho, Sam Kwong
  Proc. of the 5th International Conference on Learning and Intelligent OptimizatioN (LION’11), Springer Verlag, LNCS, p. 473–487, January 2011.
  10.1007/978-3-642-25566-3_37
  PDF | BiB

  @inproceedings{LiFK11,
    author    = {Ke Li and
                 {\'{A}}lvaro Fialho and
                 Sam Kwong},
    title     = {Multi-Objective Differential Evolution with Adaptive Control of Parameters
                 and Operators},
    booktitle = {Learning and Intelligent Optimization - 5th International Conference,
                 {LION} 5, Rome, Italy, January 17-21, 2011. Selected Papers},
    series    = {Lecture Notes in Computer Science},
    volume    = {6683},
    pages     = {473--487},
    publisher = {Springer},
    year      = {2011},
    url       = {https://doi.org/10.1007/978-3-642-25566-3\_37},
    doi       = {10.1007/978-3-642-25566-3\_37},
    timestamp = {Tue, 14 May 2019 10:00:51 +0200},
    biburl    = {https://dblp.org/rec/conf/lion/LiFK11.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2010 #

Journal #

• A Novel Slicing Based Algorithm to Calculate Hypervolume for Multi-Objective Optimization Problems
  Ke Li, Jinhua Zheng, Miqing Li, Cong Zhou, Hui Lv
  ICIC Express Letters: An International Journal of Research and Surveys, 4(4): 1113–1120, 2010.
  PDF | BiB

  @article{LiZLZL10,
    author    = {Ke Li and
                 Jinhua Zheng and
                 Miqing Li and
                 Cong Zhou and
                 Hui Lv},
    title     = {A Novel Slicing Based Algorithm to Calculate Hypervolume for Multi-Objective Optimization Problems},
    journal   = {ICIC Express Letters: An International Journal of Research and Surveys},
    volume    = {4},
    number    = {4},
    year      = {2010},
    pages     = {1133-1120}
  }

Conference #

• Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization
  Miqing Li, Jinhua Zheng, Ke Li, Qizhao Yuan, Ruimin Shen
  Proc. of the 11th International Conference on Parallel Problem Solving from Nature (PPSN’10), Springer Verlag, LNCS, volume 6238: p. 647–656, September 2010.
  10.1007/978-3-642-15844-5_65
  PDF | BiB

  @inproceedings{LiZLYS10,
      author    = {Miqing Li and
                   Jinhua Zheng and
                   Ke Li and
                   Qizhao Yuan and
                   Ruimin Shen},
      title     = {Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective
          Optimization},
      booktitle = {PPSN'10: Proc. of 11th International Conference on Parallel Problem Solving from Nature},
      pages     = {647--656},
      year      = {2010},
      url       = {https://doi.org/10.1007/978-3-642-15844-5\_65},
      doi       = {10.1007/978-3-642-15844-5\_65},
      timestamp = {Wed, 14 Nov 2018 10:55:35 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/ppsn/LiZLYS10},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• A Grid-based Fitness Strategy for Evolutionary Many-Objective Optimization
  Miqing Li, Jinhua Zheng, Ruimin Shen, Ke Li, Qizhao Yuan
  Proc. of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO’10), ACM Press: p. 463–470, July 2010.
  10.1145/1830483.1830570
  PDF | BiB

  @inproceedings{LiZSLY10,
    author    = {Miqing Li and
                 Jinhua Zheng and
                 Ruimin Shen and
                 Ke Li and
                 Qizhao Yuan},
    title     = {A grid-based fitness strategy for evolutionary many-objective optimization},
    booktitle = {Genetic and Evolutionary Computation Conference, {GECCO} 2010, Proceedings,
                 Portland, Oregon, USA, July 7-11, 2010},
    pages     = {463--470},
    publisher = {{ACM}},
    year      = {2010},
    url       = {https://doi.org/10.1145/1830483.1830570},
    doi       = {10.1145/1830483.1830570},
    timestamp = {Tue, 06 Nov 2018 11:06:40 +0100},
    biburl    = {https://dblp.org/rec/conf/gecco/LiZSLY10.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }

2009 #

Conference #

• A Novel Algorithm for Non-dominated Hypervolume-based Multiobjective Optimization
  Ke Li, Jinhua Zheng, Miqing Li, Cong Zhou, Hui Lv
  Proc. of 2009 IEEE International Conference on Systems, Mans and Cybernetics (SMC’09), IEEE Press: p. 5220–5226, December 2009.
  10.1109/ICSMC.2009.5345983
  PDF | BiB

  @inproceedings{LiZLZL09,
      author    = {Ke Li and
                   Jinhua Zheng and
                   Miqing Li and
                   Cong Zhou and
                   Hui Lv},
      title     = {A Novel Algorithm for Non-dominated Hypervolume-based Multiobjective
          Optimization},
      booktitle = {SMC'09: Proc. of the 2009 {IEEE} International Conference on Systems, Man
          and Cybernetics},
      pages     = {5220--5226},
      year      = {2009},
      url       = {https://doi.org/10.1109/ICSMC.2009.5345983},
      doi       = {10.1109/ICSMC.2009.5345983},
      timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/smc/LiZLZL09},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• An Spanning Tree Based Method For Pruning Non-Dominated Solutions in Multi- Objective Optimization Problems
  Miqing Li, Jinhua Zheng, Ke Li, Jun Wu, Guixia Xiao
  Proc. of 2009 IEEE International Conference on Systems, Mans and Cybernetics (SMC’09), IEEE Press: p. 4882–4887, December 2009.
  10.1109/ICSMC.2009.5346322
  PDF | BiB

  @inproceedings{LiZLWX09,
      author    = {Miqing Li and
                   Jinhua Zheng and
                   Ke Li and
                   Jun Wu and
                   Guixia Xiao},
      title     = {An Spanning Tree Based Method For Pruning Non-Dominated Solutions
          in Multi-Objective Optimization Problems},
      booktitle = {SMC'09: Proc. of the 2009 {IEEE} International Conference on Systems, Man
          and Cybernetics},
      pages     = {4882--4887},
      year      = {2009},
      url       = {https://doi.org/10.1109/ICSMC.2009.5346322},
      doi       = {10.1109/ICSMC.2009.5346322},
      timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/smc/LiZLWX09},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• Objective Reduction based on the Least Square Method for Large-dimensional Multiobjective Optimization Problem
  Cong Zhou, Jinhua Zheng, Ke Li, Hui Lv
  Proc. of the 5th International Conference on Natural Computation (ICNC’09), IEEE Press: p. 350–354, August 2009.
  10.1109/ICNC.2009.40
  PDF | BiB

  @inproceedings{ZhouZLL09,
      author    = {Cong Zhou and
                   Jinhua Zheng and
                   Ke Li and
                   Hui Lv},
      title     = {Objective Reduction Based on the Least Square Method for Large-Dimensional
          Multi-objective Optimization Problem},
      booktitle = {ICNC'09: Proc. of the 5th International Conference on Natural Computation},
      pages     = {350--354},
      year      = {2009},
      url       = {https://doi.org/10.1109/ICNC.2009.40},
      doi       = {10.1109/ICNC.2009.40},
      timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/icnc/ZhouZLL09},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• The Convergence Analysis of Genetic Algorithm based on Space Mating
  Hui Lv, Jinhua Zheng, Jun Wu, Cong Zhou, Ke Li
  Proc. of the 5th International Conference on Natural Computation (ICNC’09), IEEE Press: p. 557–562, August 2009.
  10.1109/ICNC.2009.39
  PDF | BiB

  @inproceedings{LvZWZL09,
      author    = {Hui Lv and
                   Jinhua Zheng and
                   Jun Wu and
                   Cong Zhou and
                   Ke Li},
      title     = {The Convergence Analysis of Genetic Algorithm Based on Space Mating},
      booktitle = {ICNC'09: Proc. of 5th International Conference on Natural Computation},
      pages     = {557--562},
      year      = {2009},
      url       = {https://doi.org/10.1109/ICNC.2009.39},
      doi       = {10.1109/ICNC.2009.39},
      timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/icnc/LvZWZL09},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  

• An Improved Multi-objective Evolutionary Algorithm based on Differential Evolution
  Ke Li, Jinhua Zheng
  Proc. of 2009 WRI World Congress on Computer Science and Information Engineering (CSIE’09), IEEE Press: p. 825–830, March 2009.
  10.1109/CSIE.2009.181
  PDF | BiB

  @inproceedings{LiZZL09,
      author    = {Ke Li and
                   Jinhua Zheng and
                   Cong Zhou and
                   Hui Lv},
      title     = {An Improved Differential Evolution for Multi-objective Optimization},
      booktitle = {CSIE'09: Proc. of the 2009 {WRI} World Congress on Computer Science and Information Engineering},
      pages     = {825--830},
      year      = {2009},
      url       = {https://doi.org/10.1109/CSIE.2009.181},
      doi       = {10.1109/CSIE.2009.181},
      timestamp = {Tue, 28 Nov 2017 16:18:09 +0100},
      biburl    = {https://dblp.org/rec/bib/conf/csie/LiZZL09},
      bibsource = {dblp computer science bibliography, https://dblp.org}
  }