Entropy and mutual information can improve fitness evaluation in coevolution of neural networks

Accurate fitness estimates are notoriously difficult to attain in cooperative coevolution, as it is often unclear how to reward the individual parts given an evaluation of the evolved system as a whole. This is particularly true for cooperative approaches to neuroevolution, where neurons or neuronal groups are highly interdependent. In this paper we investigate this problem in the context of evolving neural networks for unstable control problems. We use measures from information theory and neuroscience to reward neurons in a neural network based on their degree of participation in the behavior of the network as a whole. In particular, we actively seek networks with high complexity and little redundancy, and argue that this can lead to efficient evolution of robust controllers. Preliminary results support this claim, and indicate that measures from information theory may provide meaningful information about the role of each neuron in a network.