Evolving Crossover, Mutation and Training rates in Population Of Neural

This paper describes a method of determining the rates of crossover, mutation and training employed in the evolution of a population of neural networks. The genetic codes of the population are modied to include rate data which evolves with the population to attain optimum levels. We compare these results to experiments performed to determine optimum rate values by trial and error