A new multiobjective genetic programming for extraction of design information from non-dominated solutions

Tomoaki Tatsukawa, Taku Nonomura, Akira Oyama, Kozo Fujii

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimization problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.

本文言語English
ホスト出版物のタイトルEvolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings
ページ528-542
ページ数15
DOI
出版ステータスPublished - 2013 4 3
外部発表はい
イベント7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 - Sheffield, United Kingdom
継続期間: 2013 3 192013 3 22

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7811 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013
国/地域United Kingdom
CitySheffield
Period13/3/1913/3/22

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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