Evolutionary learning strategy using bug-based search

Authors:
Hitoshi Iba;Tetsuya Higuchi;Hugo De Garis;Taisuke Sato
Affiliations:
Machine Inference Section, Electrotechnical Laboratory, Tsukuba-city, Ibaraki, Japan;Computational Models Section, Electrotechnical Laboratory, Tsukuba-city, Ibaraki, Japan;Brain Builder Group, ATR Human Information Processing, Research Laboratories, Seiko-cho, Soraku-gun, Kyoto, Japan;Machine Inference Section, Electrotechnical Laboratory, Tsukuba-city, Ibaraki, Japan
Venue:
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Year:
1993

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Abstract

We introduce a new approach to GA (Genetic Algorithms) based problem solving. Earlier GAs did not contain local search (i.e. hill climbing) mechanisms, which led to optimization difficulties, especially in higher dimensions. To overcome such difficulties, we introduce a "bug-based" search strategy, and implement a system called BUGS2. The ideas behind this new approach are derived from biologically realistic bug behaviors. These ideas were confirmed empirically by applying them to some optimization and computer vision problems.