This chapter investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. The evolutionary and operational dynamics of controller evolution is analyzed to provide an insight into how the best controller emerges from the artificial evolution and how it generates the emergent walking behavior in the creature. A comparison between Pareto optimal controllers showed that artificial neural networks (ANNs) with varying numbers of hidden units resulted in noticeably different locomotion behaviors. We also found that a much higher level of sensory-motor coordination was present in the best evolved controller. Finally we investigated the effects of environmental, morphological and nervous system changes on the artificial creature's behavior and found that certain changes are detrimental to the creature's locomotion capability.
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