A Learning Classifier System Based on Genetic Network Programming

被引：6

作者：

Li, Xianneng ^{[1
]}

Hirasawa, Kotaro ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Informat Prod & Syst, Tokyo, Japan

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013) | 2013年

关键词：

learning classifier systems; genetic network programming; niching; fitness sharing; reinforcement learning; ALGORITHM;

D O I：

10.1109/SMC.2013.229

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1323 / 1328

页数：6

共 50 条

[41] Multi-Car Elevator System using Genetic Network Programming
Yu, Lu
Zhou, Jin
Mabu, Shingo
Shimada, Kaoru
Hirasawa, Kotaro
Markon, Sandor
[J]. 2008 PROCEEDINGS OF SICE ANNUAL CONFERENCE, VOLS 1-7, 2008, : 116 - +
[42] A Reinforcement Learning Agent based on Genetic Programming and Universal Search
Paul, Swarna Kamal
Bhaumik, Parama
[J]. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS 2020), 2020, : 122 - 128
[43] A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning
Mabu, Shingo
Hirasawa, Kotaro
Hu, Jinglu
[J]. EVOLUTIONARY COMPUTATION, 2007, 15 (03) : 369 - 398
[44] Continuous probabilistic model building genetic network programming using reinforcement learning
Li, Xianneng
Hirasawa, Kotaro
[J]. APPLIED SOFT COMPUTING, 2015, 27 : 457 - 467
[45] SemiSupervised Learning for Class Association Rule Mining Using Genetic Network Programming
Mabu, Shingo
Higuchi, Takuro
Kuremoto, Takashi
[J]. IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2020, 15 (05) : 733 - 740
[46] Genetic network programming with reinforcement learning for generating agent behavior in the benchmark problems
Mabu, S
Hirasawa, K
Hu, JL
[J]. SICE 2004 ANNUAL CONFERENCE, VOLS 1-3, 2004, : 918 - 923
[47] An effective classifier based on convolutional neural network and regularized extreme learning machine
He, Chunmei
Kang, Hongyu
Yao, Tong
Li, Xiaorui
[J]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (06) : 8309 - 8321
[48] Explanatory rule extraction based on the trained neural network and the genetic programming
Lu, JJ
Tokinaga, S
Ikeda, Y
[J]. JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN, 2006, 49 (01) : 66 - 82
[49] Fuzzy Classification Rule Mining Based on Genetic Network Programming Algorithm
Taboada, Karla
Mabu, Shingo
Gonzales, Eloy
Shimada, Kaoru
Hirasawa, Kotaro
[J]. 2009 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2009), VOLS 1-9, 2009, : 3860 - 3865
[50] Learning-based correspondence classifier with self-attention hierarchical network
Chu, Mingfan
Ma, Yong
Mei, Xiaoguang
Huang, Jun
Fan, Fan
[J]. APPLIED INTELLIGENCE, 2023, 53 (20) : 24360 - 24376

← 1 2 3 4 5 →