Effects of excessive elitism on the evolution of artificial creatures with NEAT

This paper proposes a simple method based on a novel use of elitism to increase the population size of artificial creatures while minimizing evaluation cost. This can contribute to preventing premature convergence of the population. We propose the “Excessive Elitism (EE)” method by modifying elitism in HyperNEAT (Hypercube-based NeuroEvolution of Augmenting Topologies), which is an evolutionary algorithm commonly used to evolve genotype [i.e., Compositional Pattern Producing Network (CPPN)] of artificial creatures. In EE, the evaluated fitness of best-fit individuals will be succeeded and reused instead of being re-evaluated during subsequent fitness evaluations, thereby reducing the evaluation cost if the elite size is excessive. Notably, EE also disables speciation and fitness sharing, serving to simplify the population structure and reduce complexity. In a 3D multi-agent environment, we evolved the morphology and behavior of artificial creatures with a simple target approach task. We assumed a baseline case (EE (2, 20)) in which a small population size was used due to the strong limitation of the evaluation cost and adopted a normal small elite size. This often led to premature convergence of the population to suboptimal individuals who could not reach the target. However, with the application of EE, the population was capable of evolving to reach the target, maintaining an evaluation cost comparable to EE (2, 20). We demonstrate that EE method serves as a simpler alternative to speciation for diversity preservation, capable of enhancing both the average and optimal fitness of a population, thus preventing premature convergence at a minimal evaluation cost. Further research in complex environments is required to fully uncover the potential and limitations of this method.