Extended rule-based genetic network programming

Xianneng Li; Kotaro Hirasawa

doi:10.1145/2464576.2464655

Extended rule-based genetic network programming

Xianneng Li, Kotaro Hirasawa

Information, Production and Systems Research Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decisionmaking 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 "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.

Original language	English
Title of host publication	GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Pages	155-156
Number of pages	2
DOIs	https://doi.org/10.1145/2464576.2464655
Publication status	Published - 2013
Event	15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 - Amsterdam Duration: 2013 Jul 6 → 2013 Jul 10

Other

Other	15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013
City	Amsterdam
Period	13/7/6 → 13/7/10

Keywords

Genetic network programming
Learning classifier systems
XrGNP

ASJC Scopus subject areas

Computational Mathematics

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10.1145/2464576.2464655

Cite this

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title = "Extended rule-based genetic network programming",

abstract = "Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decisionmaking 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 {"}if-then{"} decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.",

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author = "Xianneng Li and Kotaro Hirasawa",

year = "2013",

doi = "10.1145/2464576.2464655",

language = "English",

isbn = "9781450319645",

pages = "155--156",

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note = "15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 ; Conference date: 06-07-2013 Through 10-07-2013",

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T1 - Extended rule-based genetic network programming

AU - Li, Xianneng

AU - Hirasawa, Kotaro

PY - 2013

Y1 - 2013

N2 - Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decisionmaking 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 "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.

AB - Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decisionmaking 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 "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.

KW - Genetic network programming

KW - Learning classifier systems

KW - XrGNP

UR - http://www.scopus.com/inward/record.url?scp=84882420291&partnerID=8YFLogxK

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U2 - 10.1145/2464576.2464655

DO - 10.1145/2464576.2464655

M3 - Conference contribution

AN - SCOPUS:84882420291

SN - 9781450319645

SP - 155

EP - 156

BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion

T2 - 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

Y2 - 6 July 2013 through 10 July 2013

ER -

Extended rule-based genetic network programming

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Keywords

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