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Is a Self-Adaptive Pareto Approach Beneficial for Controlling Embodied Virtual Robots?
Jason Teo and Hussein A. Abbass
Artificial Life and Adaptive Robotics (A.L.A.R.) Lab
School of Computer Science, University of New South Wales
Australian Defence Force Academy Campus
Canberra, Australia
{j.teo,h.abbass}@adfa.edu.au
Abstract. A self-adaptive Pareto Evolutionary Multi-objective Optimization (EMO) algorithm is proposed for evolving controllers for a virtually embodied robot. The main contribution of the self-adaptive Pareto approach is its ability to produce controllers with different locomotion capabilities in a single run, therefore reducing the evolutionary computational cost significantly. The aim of this paper is to verify this hypothesis.
LNCS 2724, p. 1612 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003