Parameter setting in parallel genetic algorithms

Parallel genetic algorithms (GAs) have numerous parameters that affect their efficiency and accuracy. Traditionally, these parameters have been studied using empirical studies whose generality and limitations are difficult to assess. This chapter reviews existing theoretical models that predict the effects of the parameters. The models are used to examine the effect of communication topologies, migration rates, population sizing, and the choice of migrants and the individuals they replace in the receiving populations. The models should help practitioners make informed decisions about the setting of parameters of parallel GAs.