Mutual information-based fitness functions for evolutionary circuit synthesis

Entropy-based measures, such as mutual information and normalized mutual information are investigated as tools for similarity measures between the target and evolving circuit. Three fitness functions are built over a primitive one. We show that the search landscape of normalized mutual information looks more amenable for evolutionary computation algorithms than simple mutual information. The evolutionary synthesized circuits are compared to the known optimum size. A discussion of the potential of the information-theoretical approach is given.

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