Is a Self-Adaptive Pareto Approach Beneficial for Controlling Embodied Virtual Robots?
نویسندگان
چکیده
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.
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