Promoting diversity using migration strategies in distributed genetic algorithms

Abstract

This paper presents a new migration strategy that improves the overall quality of solutions in a distributed genetic algorithm (DGA) involving a number of concurrently evolving populations. The idea behind this improvement is to incorporate a diversity guided selection mechanism that selects a diverse set of individuals for migration from the evolving populations. To accompany this selection mechanism an alternative replacement policy which replaces individuals that have more than one of their copies present in the population (clones) is also investigated. This increases diversity within a population and reduces premature convergence. Results show that it leads to a better performance when compared with the send-best-replace-worst strategy.