Evolving recurrent neural controllers for sequential tasks: a parallel implementation

Evolution of complex behaviours requires a careful selection of genetic algorithm parameters and a large number of computations. In this paper, we considered evolution of recurrent neural controllers for nonMarkovian sequential tasks using a regional model genetic algorithm. The subpopulations apply different strategies and compete with each other. Simulation and experimental results using cyber rodent robot indicate that regional model outperformed single population genetic algorithm by distributing the genetic resources effectively as different strategies successful during the course of evolution.