On the design and analysis of competent GAs

We study two recent theoretical models—a population-sizing model and a convergence model—and examine their assumptions to gain insights about the conditions under which GAs work well. We use these insights to formulate several design rules to develop competent GAs for practical problems. We then use these rules to design a GA that solves the map-labeling problem, an NP-hard problem of real-world significance. Finally, we test whether the fact that our GA followed the design rules inspired by the theoretical models results in a scale-up behavior as predicted by these models. Experiments show that this is indeed the case.

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