In this work, a method is presented for analysis of Markov chains modeling evolutionary algorithms through use of a suitable quotient construction. Such a notion of quotient of a Markov chain is frequently referred to as “coarse graining” in the evolutionary computation literature. We shall discuss the construction of a quotient of an irreducible Markov chain with respect to an arbitrary equivalence relation on the state space. The stationary distribution of the quotient chain is “coherent” with the stationary distribution of the original chain. Although the transition probabilities of the quotient chain depend on the stationary distribution of the original chain, we can still exploit the quotient construction to deduce some relevant properties of the stationary distribution of the original chain. As one application, we shall establish inequalities that describe how fast the stationary distribution of Markov chains modeling evolutionary algorithms concentrates on the uniform populations as the mutation rate converges to 0. Further applications are discussed. One of the results related to the quotient construction method is a significant improvement of the corresponding result of the authors’ previous conference paper [Mitavskiy et al. (2006) In: Simulated Evolution and Learning, Proceedings of SEAL 2006, Lecture Notes in Computer Science v. 4247, Springer Verlag, pp 726–733]. This papers implications are all strengthened accordingly.
Markov chain Stationary distribution Quotient Coarse graining Evolutionary algorithm Uniform population Asymptotics Mutation rate Selection pressure