Elucidating the Benefits of A Self-Adaptive Pareto EMO Approach for Evolving Legged Locomotion in Artificial Creatures

A self-adaptive Pareto Evolutionary Multiobjective Optimization (EMO) algorithm based on differential evolution is proposed for evolving locomotion controllers in an artificially embodied legged creature. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost dramatically. Moreover, the performance of our proposed Pareto EMO algorithm was found to be comparable against a current state-of-the-art Pareto EMO algorithm, the NSGA-II algorithm, for evolving legged locomotion controllers.