Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms

Zhang，Jinyuan; Ishibuchi，Hisao; Shang，Ke; He，Linjun; Pang，Lie Meng; Peng，Yiming

10.1145/3449639.3459396

2021-06-26

2nd Genetic and Evolutionary Computation Conference (GECCO)

GECCO 2021 - Proceedings of the 2021 Genetic and Evolutionary Computation Conference

485-492

JUL 10-14, 2021

null,null,ELECTR NETWORK

1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES

ASSOC COMPUTING MACHINERY

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.

Environmental selection Fuzzy classifier Multiobjective evolutionary optimization Surrogate models

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[61876075]

Computer Science ; Operations Research & Management Science ; Mathematics

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Operations Research & Management Science ; Mathematics, Applied

WOS:000773791800058

20212910647383

Cell proliferation ; Function evaluation ; Fuzzy sets ; Population statistics ; Quality control

Biology:461.9 ; Quality Assurance and Control:913.3 ; Numerical Methods:921.6

2-s2.0-85110172341

Scopus

Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Zhang，Jinyuan,Ishibuchi，Hisao,Shang，Ke,et al. Environmental selection using a fuzzy classifier for multiobjective evolutionary algorithms[C]. 1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES:ASSOC COMPUTING MACHINERY,2021:485-492.