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 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.