Comparing Some Graph Crossover in Genetic Network Programming

Hironobu Katagiri; Kotaro Hirasawa; Jingl Hu; Junichi Murata

doi:10.11499/sicep.2002.0.279.0

SICE Annual Conference 2002

DOI https://doi.org/10.11499/sicep.2002.0.279.0

Conference information

Host: The Society of Instrument and Control Engineers

Co-host: IEEE/Industiral Electronic Society, IEEE/Robotics and Automation Society, IEEE/Control System Society

Comparing Some Graph Crossover in Genetic Network Programming

Hironobu Katagiri, Kotaro Hirasawa, Jingl Hu, Junichi Murata

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Keywords: Crossover, Graph Representation, Genetic Network Programming

CONFERENCE PROCEEDINGS FREE ACCESS

Pages 279

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Abstract

In this paper, we have studied crossover for graph-based programs. Graph crossover is unsatisfactory on several counts to divide and combine graphs unlike with string or tree crossover, because relatively large number of random factors should be inevitable to operate crossover. In this paper, we have compared the performance of several crossover operators for Genetic Network Programming experimentally. The experimental results show the advantages and drawbacks of each method.

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