Multi-objectivity for brain-behavior evolution of a physically-embodied organism

In this paper, we present a pareto multi-objective approach for evolving the behavior and brain (an artificial neural network (ANN)) of embodied artificial creatures. We will attempt to simultaneously minimize the network size while maximizing horizontal locomotion. A variety of network sizes and behaviors were generated by the pareto approach. The best networks exhibited a higher level of sensory-motor coordination and the creature was able to maintain the walking behavior under different environmental setups.

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