Genetic network programming with learning and evolution for adapting to dynamical environments

Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

研究成果: Paper査読

16 被引用数 (Scopus)

抄録

A new evolutionary algorithm named « genetic network programming, GNP» has been proposed. GNP represents its solutions as network structures, which can improve the expression and search ability. Since GA, GP, and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with learning and evolution in order to adapt to a dynamical environment quickly. Learning algorithm improves search speed for solutions and evolutionary algorithm enables GNP to search wide solution space efficiently.

本文言語English
ページ69-76
ページ数8
DOI
出版ステータスPublished - 2003
イベント2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia
継続期間: 2003 12 82003 12 12

Conference

Conference2003 Congress on Evolutionary Computation, CEC 2003
CountryAustralia
CityCanberra, ACT
Period03/12/803/12/12

ASJC Scopus subject areas

  • Computational Mathematics

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