A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization
Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand...
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| Vydané v: | IEEE transactions on evolutionary computation Ročník 27; číslo 6; s. 1941 - 1961 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
| On-line prístup: | Získať plný text |
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| Abstract | Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field. |
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| AbstractList | Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly, including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This article begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field. |
| Author | Liu, Songbai Tan, Kay Chen Li, Jianqiang Lin, Qiuzhen |
| Author_xml | – sequence: 1 givenname: Songbai orcidid: 0000-0003-1048-4486 surname: Liu fullname: Liu, Songbai email: songbai@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Qiuzhen orcidid: 0000-0003-2415-0401 surname: Lin fullname: Lin, Qiuzhen email: qiuzhlin@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 3 givenname: Jianqiang orcidid: 0000-0002-2208-962X surname: Li fullname: Li, Jianqiang organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 4 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen email: kctan@polyu.edu.hk organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong, SAR |
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| Snippet | Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However,... |
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| SubjectTerms | Evolution (biology) Evolutionary algorithms Evolutionary computation Generators Genetic algorithms Learnable evolutionary algorithms Machine learning machine learning (ML) Multiple objective analysis Optimization scalable multiobjective optimization Scaling Search problems Sociology Statistics Taxonomy |
| Title | A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization |
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| Volume | 27 |
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