NDLI: Rule accumulation method based on Credit Genetic Network Programming

Content Provider	IEEE Xplore Digital Library
Author	Lutao Wang Wei Xu Mabu, S. Hirasawa, K.
Copyright Year	2012
Description	Author affiliation: Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu, Kitakyushu, Fukuoka, Japan (Lutao Wang; Wei Xu; Mabu, S.; Hirasawa, K.)
Abstract	As a new promising evolutionary computation method, Genetic Network Programming (GNP) is good at generating action rules for multi-agent control in dynamic environments. However, some unimportant nodes exist in the program of GNP. These nodes serve as some redundant information which decreases the performance of GNP and the quality of the generated rules. In order to prune these nodes, this paper proposes a novel method named Credit GNP, where a credit branch is added to each node. When the credit branch is visited, the node is neglected and its function is not executed, so that the unimportant nodes could be jumped. The probability of visiting this credit branch and to which node it is jumped is determined by both evolution and Sarsa-learning, therefore, the unimportant nodes could be pruned automatically. Simulation results on the Tile-world problem show that the proposed method could get better programs and generate better and more general rules.
Starting Page	1
Ending Page	8
File Size	906611
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6253004
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economic indicators Tiles Training Testing Integrated circuits Genetics Learning
Content Type	Text
Resource Type	Article

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