On Evolutionary Optimization of Recurrent Neural Network Topologies Along with Weights

Abstract

The generation alternation model employed by one of the best neuroevolution methods, NEAT, has a drawback; it proceeds without intensively searching neighbors of parent individuals and hence it often fails in identifying solutions with minimal topologies. We propose an alternative for the model and discuss its effectiveness through the application to several non-Markovian tasks.