Modeling genetic algorithms with Markov chains

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.This research was supported by the National Science Foundation (IRI-8917545).