Sample-based Crowding method for multimodal optimization in continuous domain

Shin Ando, Einoshin Suzuki, Shigenobu Kobayashi

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

6 被引用数 (Scopus)

抄録

We propose a selection scheme called Sample-based Crowding, which is aimed to improve the performance of Genetic Algorithms for multimodal optimization in ill-scaled and locally multimodal domains. These domains can be problematic for conventional approaches, but are commonly found in real-world optimization problems. The principle of Crowding is to apply a tournament selection to a parent-child pair with a high similarity. In the Sample-based Crowding, we determine such pairs based on a statistical comparison of the fitness values, which are sampled from the region between the pairs. Further, we take into account the ranks of the parents among the sampled values in the selection process, to determine their indispensability. These measurements are scale-invariant, which enables the proposed method to search a domain without presuming the distance between the optima or the scaling and the correlation of the variables. The proposed approach is evaluated in two bench-mark problems with an ill-scaled and a locally multi-modal landscape. The proposed method has a substantial advantage in terms of comprehensiveness compared to the conventional approaches, despite the additional cost of evaluations.

本文言語英語
ホスト出版物のタイトル2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
ページ1867-1874
ページ数8
出版ステータス出版済み - 10 31 2005
外部発表はい
イベント2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, 英国
継続期間: 9 2 20059 5 2005

出版物シリーズ

名前2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
2

その他

その他2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
Country英国
CityEdinburgh, Scotland
Period9/2/059/5/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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