Fitness landscape approximation by adaptive support vector regression with opposition-based learning

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

8 被引用数 (Scopus)

抄録

We propose a method for approximating a fitness landscape using adaptive support vector regression (SVR) with opposition based learning (OBL) to enhance the evolutionary search. This method tries to resolve the complexity of the fitness landscape in the original search space by designing a suitable kernel function with an adaptive parameter tuned by OBL; This kernel projects the original search space into a higher dimensional search space with a different topological structure. The elite is obtained from the approximated fitness landscape, using the adaptive SVR to accelerate the evolutionary computation (EC) search, and the individual with the worst fitness is replaced. The merits of the proposed method are evaluated by comparing it with the fitness landscape approximated in the original, in a lower and in a higher dimensional search space.

本文言語英語
ホスト出版物のタイトルProceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
ページ1329-1334
ページ数6
DOI
出版ステータス出版済み - 2013
イベント2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, 英国
継続期間: 10 13 201310 16 2013

出版物シリーズ

名前Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

その他

その他2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
国/地域英国
CityManchester
Period10/13/1310/16/13

All Science Journal Classification (ASJC) codes

  • 人間とコンピュータの相互作用

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