Abstract
We propose a novel algorithm for the evolution of morphology and control of autonomous robots controlled by artificial neural networks. The proposed algorithm is inspired by NeuroEvolution of Augmenting Topologies (NEAT) which efficiently evolves artificial neural networks.
Large-scale experiments with simulated robots have shown that the proposed algorithm uses significantly less fitness evaluations than a standard genetic algorithm on all four tested fitness functions