In this paper we investigate whether multi-objective evolution of digital hardware components has advantages over single-objective evolution in terms of convergence and robustness. To that end, we experimentally compare a standard genetic algorithm to several multi-objective optimizers on a set of test problems. The results show that, for more complex test problems, the multi-objective optimizers TSPEA2 and NSGAII indeed outperform the single-objective genetic algorithm as they more often evolve correct circuits, and mostly with less computational effort.
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