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

大学院情報生産システム研究科

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	https://doi.org/10.1109/CEC.2003.1299558
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 8 → 2003 12 12

Conference

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

ASJC Scopus subject areas

Computational Mathematics

文献へのアクセス

10.1109/CEC.2003.1299558

フィンガープリント「Genetic network programming with learning and evolution for adapting to dynamical environments」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

@conference{bce9ee2141254ec4a0a64da9c2dc715c,

title = "Genetic network programming with learning and evolution for adapting to dynamical environments",

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.",

author = "Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",

year = "2003",

doi = "10.1109/CEC.2003.1299558",

language = "English",

pages = "69--76",

note = "2003 Congress on Evolutionary Computation, CEC 2003 ; Conference date: 08-12-2003 Through 12-12-2003",

}

TY - CONF

T1 - Genetic network programming with learning and evolution for adapting to dynamical environments

AU - Mabu, Shingo

AU - Hirasawa, Kotaro

AU - Hu, Jinglu

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=4344582812&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4344582812&partnerID=8YFLogxK

U2 - 10.1109/CEC.2003.1299558

DO - 10.1109/CEC.2003.1299558

M3 - Paper

AN - SCOPUS:4344582812

SP - 69

EP - 76

T2 - 2003 Congress on Evolutionary Computation, CEC 2003

Y2 - 8 December 2003 through 12 December 2003

ER -