文章基本信息

标题：Multimodal Search with Immune Based Genetic Programming
本地全文：下载
作者：Yoshihiko Hasegawa ; Hitoshi Iba
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2006
卷号：21
期号：2
页码：176-183
DOI：10.1527/tjsai.21.176
出版社：The Japanese Society for Artificial Intelligence
摘要：Artificial Immune System has been regarded an effective powerful optimization framework because of its powerful information processing capabilities. Natural immune system has many features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibody. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems. However, Genetic Programming with immune features which is capable of solving multimodal problems has not been proposed. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing the immunological feature so as to solve the problems with multimodal fitness landscape. We empirically show the effectiveness of our approach by applying the algorithm to the gene classification problem and the HP protein folding problem.
关键词：genetic programmming ; immune algorithm ; multimodal ; molecular diagnostics ; hp protein folding