Extension of Genetic Programming for Agent Learning

Takashi ITO  Kenichi TAKAHASHI  Michimasa INABA  

D - Abstracts of IEICE TRANSACTIONS on Information and Systems (Japanese Edition)   Vol.J98-D    No.6    pp.905-915
Publication Date: 2015/06/01
Online ISSN: 1881-0225
Type of Manuscript: Special Section PAPER (Special Section on Software Agent and Its Applications)
genetic approach,  Genetic Programming,  autonomous agent,  conditional probability,  island model,  

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This paper proposes an extension of Genetic Programming (GP) for agent learning. In order to improve the performance, GP with control node (GPCN) and its three kinds of modification have been proposed. In GPCN, an individual consists of several trees which have the number P of executions. The three kinds of modification are the conditional probability, the cross-cultural island model, and updating the value P. This paper proposes a new method that combines the conditional probability with the cross-cultural island method. Experiments are conducted to show the performance in the garbage collection problem and Santa Fe Trail problem.

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