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