Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive proce...
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| Vydáno v: | IEEE transactions on emerging topics in computational intelligence Ročník 7; číslo 4; s. 1051 - 1064 |
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| Jazyk: | angličtina |
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Piscataway
IEEE
01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2471-285X, 2471-285X |
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| Abstract | Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive process to determine the best EA for a new problem. Hence, some recent studies have been dedicated to the adaptive selection of the best operators during the search process. To address the exploration versus exploitation dilemma in operator selection, this paper proposes a novel operator selection method based on reinforcement learning. In the proposed method, the decision variables are regarded as states and the candidate operators are regarded as actions. By using deep neural networks to learn a policy that estimates the <inline-formula><tex-math notation="LaTeX">Q</tex-math></inline-formula> value of each action given a state, the proposed method can determine the best operator for each parent that maximizes its cumulative improvement. An EA is developed based on the proposed method, which is verified to be more effective than the state-of-the-art ones on challenging multi-objective optimization problems. |
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| AbstractList | Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive process to determine the best EA for a new problem. Hence, some recent studies have been dedicated to the adaptive selection of the best operators during the search process. To address the exploration versus exploitation dilemma in operator selection, this paper proposes a novel operator selection method based on reinforcement learning. In the proposed method, the decision variables are regarded as states and the candidate operators are regarded as actions. By using deep neural networks to learn a policy that estimates the [Formula Omitted] value of each action given a state, the proposed method can determine the best operator for each parent that maximizes its cumulative improvement. An EA is developed based on the proposed method, which is verified to be more effective than the state-of-the-art ones on challenging multi-objective optimization problems. Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-objective optimization, where a number of variation operators have been developed to handle the problems with various difficulties. While most EAs use a fixed operator all the time, it is a labor-intensive process to determine the best EA for a new problem. Hence, some recent studies have been dedicated to the adaptive selection of the best operators during the search process. To address the exploration versus exploitation dilemma in operator selection, this paper proposes a novel operator selection method based on reinforcement learning. In the proposed method, the decision variables are regarded as states and the candidate operators are regarded as actions. By using deep neural networks to learn a policy that estimates the <inline-formula><tex-math notation="LaTeX">Q</tex-math></inline-formula> value of each action given a state, the proposed method can determine the best operator for each parent that maximizes its cumulative improvement. An EA is developed based on the proposed method, which is verified to be more effective than the state-of-the-art ones on challenging multi-objective optimization problems. |
| Author | Zhang, Xingyi Tan, Kay Chen Li, Xiaopeng Ma, Haiping Tian, Ye Jin, Yaochu |
| Author_xml | – sequence: 1 givenname: Ye orcidid: 0000-0002-3487-5126 surname: Tian fullname: Tian, Ye email: field910921@gmail.com organization: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China – sequence: 2 givenname: Xiaopeng orcidid: 0000-0003-4387-8107 surname: Li fullname: Li, Xiaopeng email: lxp@stu.ahu.edu.cn organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 3 givenname: Haiping orcidid: 0000-0002-3115-6855 surname: Ma fullname: Ma, Haiping email: hpma@ahu.edu.cn organization: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China – sequence: 4 givenname: Xingyi orcidid: 0000-0002-5052-000X surname: Zhang fullname: Zhang, Xingyi email: xyzhanghust@gmail.com organization: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, Anhui, China – sequence: 5 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, China – sequence: 6 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu email: jin@uni-bielefeld.de organization: Faculty of Technology, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany |
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| SubjectTerms | Artificial neural networks Convergence Deep learning Evolutionary algorithm Evolutionary algorithms Machine learning multi-objective optimization Multiple objective analysis Neural networks operator selection Operators Optimization Particle swarm optimization Reinforcement learning Search process Sociology Statistics |
| Title | Deep Reinforcement Learning Based Adaptive Operator Selection for Evolutionary Multi-Objective Optimization |
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