Improving the performance of NEAT related algorithm via complexity reduction in search space

Abstract

In this paper, we focus on the learning aspect of NEAT and its variants in an attempt to solve benchmark problems through fewer generations. In NEAT, genetic algorithm is the key technique that is used to complexify artificial neural network. Crossover value, being the parameter that dictates the evolution of NEAT is reduced. Reducing crossover rate aids in allowing the algorithm to learn. This is because lesser interchange among genes ensures that patterns of genes carrying valuable information is not split or strayed during mating of two chromosomes. By tweaking the crossover parameter and with some minor modification, it is shown that the performance of NEAT can be improved. This enables NEAT algorithm to evolve slowly and retain information even while undergoing complexification. Thus, the learning process in NEAT is greatly enhanced as compared to evolution. © Springer International Publishing Switzerland 2013.