Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm

Authors:
Ting Hu;Simon Harding;Wolfgang Banzhaf
Affiliations:
Department of Computer Science, Memorial University, St. John's, Canada;Department of Computer Science, Memorial University, St. John's, Canada;Department of Computer Science, Memorial University, St. John's, Canada
Venue:
Genetic Programming and Evolvable Machines
Year:
2010

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Abstract

With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovation to many aspects of evolutionary algorithms. In this article, we focus on the effect of variable population size on accelerating evolution in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size have considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and naturally from the algorithm rather than through intentional design. To investigate the effect of variable population size in such a parallel algorithm, evolution dynamics, including fitness progression and population diversity variation, are analyzed. Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the dramatic changes in population size allow evolution to accelerate.