On the Use of Cultural Enhancement Strategies to Improve the NEAT Algorithm

Knowledge transmitted between generations by non-genetic means can be understood as culture. The capacity of individuals from certain species to teach and learn plays a fundamental role in directing the evolutionary process. The Neuroevolution of Augmenting Topologies (NEAT) framework enables evolving neural structures to iteratively solve a given learning problem. However, the NEAT approach does not consider cultural aspects in its formulation. In such a context, the aim of this paper is to propose and evaluate ways of enhancing the NEAT framework with additional learning approaches. The parameters involved in the analysis comprise the Backpropagation and the Extreme Learning Machine (ELM) learning algorithms, the individuals to be taught, the moment when culture manifests in the system, and the nature of the lessons to be learned. Empirical results on sequential learning tasks indicate that cultural enhancements, as well as some of the proposed variations, accelerate the neuroevolution convergence.