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

Shingo Mabu; Kotaro Hirasawa; Jinglu Hu

doi:10.1109/CEC.2003.1299558

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

Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Graduate School of Information, Production and Systems

Research output: Contribution to conference › Paper › peer-review

17 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 language	English
Pages	69-76
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2003.1299558
Publication status	Published - 2003
Event	2003 Congress on Evolutionary Computation, CEC 2003 - Canberra, ACT, Australia Duration: 2003 Dec 8 → 2003 Dec 12

Conference

Conference	2003 Congress on Evolutionary Computation, CEC 2003
Country/Territory	Australia
City	Canberra, ACT
Period	03/12/8 → 03/12/12

ASJC Scopus subject areas

Computational Mathematics

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10.1109/CEC.2003.1299558

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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.",

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

Abstract

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ASJC Scopus subject areas

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