Improving the performance of mutation-based evolving artificial neural networks with self-adaptive mutations.

ABSTRACT: Neuroevolution is a promising approach for designing artificial neural networks using an evolutionary algorithm. Unlike recent trending methods that rely on gradient-based algorithms, neuroevolution can simultaneously evolve the topology and weights of neural networks. In neuroevolution with topological evolution, handling crossover is challenging because of the competing conventions problem. Mutation-based evolving artificial neural network is an alternative topology and weights neuroevolution approach that omits crossover and uses only mutations for genetic variation. This study enhances the performance of mutation-based evolving artificial neural network in two ways. First, the mutation step size controlling the magnitude of the parameter perturbation is automatically adjusted by a self

SUBMITTER: Hiraga M 

PROVIDER: S-EPMC11249216 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature