Training of Neural Networks by means of Genetic Algorithms Working on very long Chromosomes

Abstract

In the neural network / genetic algorithm community, rather limited success in the training of neural networks by genetic algorithms has been reported. In a paper by Whitley (1991), he claims that, due to ``the multiple representations problem´´, genetic algorithms will not effectively be able to train multilayer perceptrons, whose chromosomal representation of its weights exceeds 300 bit's. In the following paper, by use of a ``real-life problem´´, known to be non-trivial, and by a comparison with ``classic´´ neural net training methods, I will try to show that the modest success of applying genetic algorithms to the training of perceptrons, is caused not so much by the ``multiple representations problem´´ as by the fact that problem-specific knowledge available is often ignored, thus making the problem unnecessarily tough for the genetic algorithm to solve.