Abstract
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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References
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This research was supported by the National Science Foundation (IRI-8917545).
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Nix, A.E., Vose, M.D. Modeling genetic algorithms with Markov chains. Ann Math Artif Intell 5, 79–88 (1992). https://doi.org/10.1007/BF01530781
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DOI: https://doi.org/10.1007/BF01530781