Analysis of a Non-Generational Mutationless Evolutionary Algorithm for Separable Fitness Functions

It is shown that the stochastic dynamics of nongenerational evolutionary algorithms with binary tournament selection and gene pool recombination but without mutation is closely approximated by a stochastic process consisting of several de-coupled random walks, provided the fitness function is separable in a certain sense. This approach leads to a lower bound on the population size such that the evolutionary algorithm converges to a uniform population with globally optimal individuals for a given confidence level.