Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization

Nojima, Yusuke1; Tanigaki, Yuki1; Masuyama, Naoki1; Ishibuchi, Hisao2

10.1109/SMC.2018.00135

2018

1062-922X

978-1-5386-6651-7

2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)

745-750

7-10 Oct. 2018

Miyazaki, Japan

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In recent years, evolutionary multiobjective optimization (EMO) algorithms have frequently been used for engineering problems with some conflicting objective functions to be simultaneously optimized EMO algorithms can provide a number of Pareto optimal solutions to users. Two scenarios are considered in the practical use of EMO algorithms. One is that a decision maker selects a single solution from the obtained ones after the EMO process. The other is that a decision maker utilizes the solutions to analyze the relationship between design variables and objective functions of the corresponding problem. In this paper, we apply fuzzy genetics-based machine learning to the second scenario in order to generate if-then rule-based classifiers which represent the relationship between design variables and objective functions. We also utilize this method during the EMO process to pre-screen candidate offspring solutions. The classifier detects non-promising offspring solutions. Then, they are discarded before their fitness evaluation, so that the computation resource is used only for promising solutions. We apply this method to one engineering problem and examine its effect on the search performance of an EMO algorithm.

Evolutionary multiobjective optimization data mining multiobjective fuzzy genetics-based machine learning pattern classification

其他

英语

CPCI-S ; EI

Computer Science

Computer Science, Cybernetics ; Computer Science, Information Systems

WOS:000459884800125

20191006582881

Chromosomes ; Data mining ; Decision making ; Evolutionary algorithms ; Machine learning ; Pareto principle ; Pattern recognition

Biological Materials and Tissue Engineering:461.2 ; Data Processing and Image Processing:723.2 ; Management:912.2 ; Optimization Techniques:921.5

Web of Science

1.Osaka Prefecture Univ, Dept Comp Sci & Intelligent Syst, Sakai, Osaka 5998531, Japan
2.Southern Univ Sci & Technol SUSTech, Dept Comp Sci & Engn, Shenzhen, Peoples R China

Nojima, Yusuke,Tanigaki, Yuki,Masuyama, Naoki,et al. Multiobjective Evolutionary Data Mining for Performance Improvement of Evolutionary Multiobjective Optimization[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:745-750.