Modeling genetic algorithms with Markov chains
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We model a simple genetic algorithm as a Markov chain. Our method is both complete (selection, mutation, and crossover are incorporated into an explicitly given transition matrix) and exact; no special assumptions are made which restrict populations or population trajectories. We also consider the asymptotics of the steady state distributions as population size increases.
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