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

Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to conferencePaperpeer-review

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages69-76
Number of pages8
DOIs
Publication statusPublished - 2003
Event2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia
Duration: 2003 Dec 82003 Dec 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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