A learning classifier system based on genetic network programming

Xianneng Li; Kotaro Hirasawa

doi:10.1109/SMC.2013.229

A learning classifier system based on genetic network programming

Xianneng Li, Kotaro Hirasawa

情報生産システム研究センター

研究成果: Conference contribution

8 被引用数 (Scopus)

抄録

Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

本文言語	English
ホスト出版物のタイトル	Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
ページ	1323-1328
ページ数	6
DOI	https://doi.org/10.1109/SMC.2013.229
出版ステータス	Published - 2013
イベント	2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom 継続期間: 2013 10月 13 → 2013 10月 16

Other

Other	2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
国/地域	United Kingdom
City	Manchester
Period	13/10/13 → 13/10/16

ASJC Scopus subject areas

人間とコンピュータの相互作用

文献へのアクセス

10.1109/SMC.2013.229

他のファイルとリンク

引用スタイル

@inproceedings{3d58c08bfd904b289d984e3230de3b3e,

title = "A learning classifier system based on genetic network programming",

abstract = "Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.",

keywords = "Fitness sharing, Genetic network programming, Learning classifier systems, Niching, Reinforcement learning",

author = "Xianneng Li and Kotaro Hirasawa",

year = "2013",

doi = "10.1109/SMC.2013.229",

language = "English",

isbn = "9780769551548",

pages = "1323--1328",

booktitle = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",

note = "2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 ; Conference date: 13-10-2013 Through 16-10-2013",

}

TY - GEN

T1 - A learning classifier system based on genetic network programming

AU - Li, Xianneng

AU - Hirasawa, Kotaro

PY - 2013

Y1 - 2013

N2 - Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

AB - Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

KW - Fitness sharing

KW - Genetic network programming

KW - Learning classifier systems

KW - Niching

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=84893547065&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893547065&partnerID=8YFLogxK

U2 - 10.1109/SMC.2013.229

DO - 10.1109/SMC.2013.229

M3 - Conference contribution

AN - SCOPUS:84893547065

SN - 9780769551548

SP - 1323

EP - 1328

BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

T2 - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Y2 - 13 October 2013 through 16 October 2013

ER -

A learning classifier system based on genetic network programming

抄録

Other

ASJC Scopus subject areas

文献へのアクセス

他のファイルとリンク

フィンガープリント

引用スタイル