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Fuzzy Adaptive Search Method for Genetic Programming
Yoichiro Maeda
Faculty of Information Science and Technology, Osaka Electro-Communication University 18-8, Hatsu-cho, Neyagawa, Osaka 572-8530, Japan
Received:June 11, 1998Accepted:November 8, 1998Published:April 20, 1999
Keywords:Genetic programming, Adaptive search, Fuzzy rules, Mutation rate, Crossover rate
Abstract
The greatest problem of genetic programming (GP) is the large amount of calculation. We propose two methods for an efficient GP search - RPGP reducing void searches by pruning redundant nodes in GP and FASGP enabling proper search by tuning a mutation and crossover rate by fuzzy rules. Simulation proved the effectiveness of our proposal.
Cite this article as:Y. Maeda, “Fuzzy Adaptive Search Method for Genetic Programming,” J. Adv. Comput. Intell. Intell. Inform., Vol.3 No.2, pp. 131-135, 1999.Data files:
