Empirical Studies on the Speed of Convergence of Neural Network Training using Genetic Algorithms

Hiroaki Kitano

This paper reports several experimental results on the speed of convergence of neural network training using genetic algorithms and back propagation. Recent excitement regarding genetic search lead some researchers to apply it to training neural networks. There are reports on both successful and faulty results, and, unfortunately, no systematic evaluation has been made. This paper reports results of systematic experiments designed to judge whether use of genetic algorithms provides any gain in neural network training over existing methods. Experimental results indicate that genetic search is, at best, equally efficient to faster variants of back propagation in very small scale networks, but far less efficient in larger networks.


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