A genetic algorithm with deterministic mutation based on neural network learning

Abstract

This paper presents a method for designing neural networks using a genetic algorithm (GA) with deterministic mutation (DM) based on learning. The GA presented in this paper has a large framework including DM, which is performed on the basis of the results from neural network learning. It can achieve better convergence properties than traditional GAs. This framework is an evolutional system based on mutual interaction between DM and traditional genetic operators including stochastic mutation. It is also a model of transcription and reverse transcription in DNA. We show that the present method is better than conventional GAs with respect to convergence in learning. © 1998 Scripta Technica. Syst. Comp Jpn, 29(3): 10–17, 1998