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Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach
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文献类型:期刊论文
标题:Niching genetic network programming with rule accumulation for decision making: An evolutionary rule-based approach
作者:Li, Xianneng[1];Yang, Meihua[1];Wu, Shizhe[1]
机构:
通讯作者:Li, XN (reprint author), Dalian Univ Technol, Fac Management & Econ, 2 Linggong Rd, Dalian 116024, Peoples R China.
来源信息:年:2018  卷:114  页码范围:374-387  
期刊信息:EXPERT SYSTEMS WITH APPLICATIONS影响因子和分区  ISSN:0957-4174
增刊:正刊
收录情况:SCIE(WOS:000446949300028)  
所属部门:管理与经济学部
语言:外文
发表时间:2018-12-30
全文链接:DOI百度学术
人气指数:154
浏览次数:154
基金:National Natural Science Foundation of China [71601028, 71671024, 71421001, 71431002]; Fundamental Research Funds for the Central Universities [DUT17JC12]; Economic & Social Development Foundation of Liaoning [2018lslktqn-015]
关键词:Decision making; Genetic network programming; Learning classifier system; Niching; Reinforcement learning; Rule accumulation
摘要:As one of the most important research branches of evolutionary computation (EC), learning classifier system (LCS) is dedicated to discover decision making classifiers ("IF-THEN" type rules) via evolution and learning. Recent advances in LCS have shown distinguished generalization property over traditional approaches. In this paper, a novel LCS named niching genetic network programming with rule accumulation (nGNP-RA) is proposed. The unique features of the proposal arise from the following three points: First, it utilizes an advanced graph-based EC named GNP as the rule generator, resulting higher knowledge representation ability than traditional genetic algorithm (GA)-based LCSs; Second, a novel niching mechanism is developed in GNP to encourage the discovery of high-quality diverse rules; Third, a novel reinforcement learning (RL)-based mechanism is embedded to assign accurate credits to the discovered rules. To verify the effectiveness and robustness of nGNP-RA over traditional systems, two decision making testbeds are applied, including the benchmark tileworld problem and the real mobile robot control application. (C) 2018 Elsevier Ltd. All rights reserved.
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