Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms.

Daniel J. Simon, Cleveland State University
Rick Rarick
Mehmet Ergezer
Dawei Du


We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems.