research-article

Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms

Authors:

Hisao Ishibuchi,

Yiming PengAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 485 - 492

https://doi.org/10.1145/3449639.3459396

Published: 26 June 2021 Publication History

Abstract

The quality of solutions in multiobjective evolutionary algorithms (MOEAs) is usually evaluated by objective functions. However, function evaluations (FEs) are usually time-consuming in real-world problems. A large number of FEs limit the application of MOEAs. In this paper, we propose a fuzzy classifier-based selection strategy to reduce the number of FEs of MOEAs. First, all evaluated solutions in previous generations are used to build a fuzzy classifier. Second, the built fuzzy classifier is used to predict each unevaluated solution's label and its membership degree. The reproduction procedure is repeated to generate enough offspring solutions (classified as positive by the classifier). Next, unevaluated solutions are sorted based on their membership degrees in descending order. The same number of solutions as the population size are selected from the top of the sorted unevaluated solutions. Then, the best half of the chosen solutions are selected and stored in the new population without evaluations. The other half solutions are evaluated. Finally, the evaluated solutions are used together with evaluated current solutions for environmental selection to form another half of the new population. The proposed strategy is integrated into two MOEAs. Our experimental results demonstrate the effectiveness of the proposed strategy on reducing FEs.

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Cited By

Zhang JHe LIshibuchi H(2022)Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319566827:6(1575-1589)Online publication date: 2-Aug-2022
https://dl.acm.org/doi/10.1109/TEVC.2022.3195668

Index Terms

Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms
      2. Mixed discrete-continuous optimization
        Bio-inspired optimization

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cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

June 2021

1219 pages

ISBN:9781450383509

DOI:10.1145/3449639

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Copyright © 2021 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2021

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Funding Sources

Guangdong Provincial Key Laboratory
National Natural Science Foundation of China
the Program for Guangdong Introducing Innovative and Enterpreneurial Teams
the Program for University Key Laboratory of Guangdong Province
Shenzhen Science and Technology Program

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GECCO '21

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SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Zhang JHe LIshibuchi H(2022)Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.319566827:6(1575-1589)Online publication date: 2-Aug-2022
https://dl.acm.org/doi/10.1109/TEVC.2022.3195668

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