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. This paper presents an alternative for the model and discusses its effectiveness through the application to several non-Markovian tasks.