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

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@Article{hu:2010:GPEM,

• author = "Ting Hu and Simon Harding and Wolfgang Banzhaf",
• title = "Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2010",
• volume = "11",
• number = "2",
• pages = "205--225",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Variable population size, Population bottleneck, Evolution acceleration, Parallel computing, GPU",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-010-9105-2",
• size = "21 pages",
• 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.",
• notes = "Not GP, preparation for it? 'a simulation of the APEA algorithm', compiled, CUDA, OneMax problem, Spears multi-model problem. 'Individuals are encoded as binary strings for both problems'",

}

Genetic Programming entries for Ting Hu Simon Harding Wolfgang Banzhaf

Citations