The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems

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@InProceedings{Cavill:Tpo:cec2005,
  author =       "Rachel Cavill and Stephen L. Smith and Andy Tyrrell",
  title =        "The performance of polyploid evolutionary algorithms
                 is improved both by having many chromosomes and by
                 having many copies of each chromosome on symbolic
                 regression problems",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
                 Computation",
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and Bob McKay and 
                 Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Gunther Raidl and 
                 Kay Chen Tan and Ali Zalzala",
  pages =        "935--941",
  address =      "Edinburgh, Scotland, UK",
  month =        "2-5 " # sep,
  publisher =    "IEEE Press",
  volume =       "1",
  keywords =     "genetic algorithms, genetic programming, biology,
                 cellular biophysics, evolutionary computation,
                 regression analysis, multiple chromosomes, polyploid
                 evolutionary algorithm, symbolic regression problem",
  ISBN =         "0-7803-9363-5",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=1",
  URL =          "http://ieeexplore.ieee.org/servlet/opac?punumber=10417",
  doi =          "doi:10.1109/CEC.2005.1554783",
  abstract =     "This paper presents important new findings for a new
                 method for evolving individual programs with multiple
                 chromosomes. Previous results have shown that evolving
                 individuals with multiple chromosomes produced improved
                 results over evolving individuals with a single
                 chromosome. The multiple chromosomes are organised
                 along two axes; there are a number of different
                 chromosomes and a number of copies of each chromosome.
                 This paper investigates the effects which these two
                 axes have on the performance of the algorithm; whether
                 the improvement in performance comes from just one of
                 these features or whether it is a combination of them
                 both",
  notes =        "Last author is NOT Terrell",
}

Genetic Programming entries for Rachel Cavill Stephen L Smith Andrew M Tyrrell