Neuroevolution of Augmenting Topologies for Artificial Evolution: A Case Study of Kinesis of Creature in the Various Mediums

Abstract

The motivation of the present study is to evolve virtual creatures in a diverse simulated 3D environment. The proposed scheme is based on the artificial evolution using the Neuro Evolution of Augmenting Topologies (NEAT) algorithm to educe a neural network that controls the muscle forces of the artificial creatures. The morphologies of the creatures are established using the Genetic Algorithm (GA) method based on the distance metrics fitness function. The concept of damaging crossover of neural networks and genetic language for the morphology of creatures has been considered in the morphologies of the artificial creature. Creatures with certain morphological traits consume a large time to optimize their kinetics, thus they are placed in a separate species to limit the search. The simulation results in the significant kinetics of artificial creatures (2-5 limbs) in virtual mediums with varying dynamic and static coefficients of friction (0.0-4.0). The motion of artificial creatures in the simulated medium was determined at different angles and demonstrated in the 3D space.