A tensor-based mutation operator for Neuroevolution of Augmenting Topologies (NEAT)

In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the population throughout the evolutionary process. Various kinds of mutation may occur over time, typically depending on a fixed probability value called mutation rate. In this work we make use of a novel data-science approach in order to adaptively generate mutation rates for each locus to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. The trail of high quality candidate solutions obtained during the search process is represented as a third-order tensor; factorization of such a tensor reveals the latent relationship between solutions, determining the mutation probability which is likely to yield improvement at each locus. The single pole balancing problem is used as case study to analyze the effectiveness of the proposed approach. Results show that the tensor approach improves the performance of the standard NEAT algorithm for the case study.