A statistical comparison study between Genetic Algorithms and Bayesian Optimization Algorithms

Abstract

The Genetic Algorithm (GA) is a search and optimization technique based on the mechanism of evolution. In this paper, we propose new statistical indices which are based on the concepts of crossover and mutation, used in GAs, to analyze the behavior of the population based optimization techniques. We also show simple results of comparison studies between GAs and the Bayesian Optimization Algorithm (BOA), a well-known Estimation of Distribution Algorithms (EDAs).