Evolving Ensembles: What Can We Learn from Biological Mutualisms?

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@InProceedings{lones2015evolving,
  author =       "Michael A. Lones and Stuart E. Lacy and 
                 Stephen L. Smith",
  title =        "Evolving Ensembles: What Can We Learn from Biological
                 Mutualisms?",
  booktitle =    "10th International Conference on Information
                 Processing in Cells and Tissues, IPCAT 2015",
  year =         "2015",
  editor =       "Michael Lones and Andy Tyrrell and Stephen Smith and 
                 Gary Fogel",
  volume =       "9303",
  series =       "LNCS",
  pages =        "52--60",
  address =      "San Diego, CA, USA",
  month =        sep # " 14-16",
  publisher =    "Springer International Publishing",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-23108-2",
  DOI =          "doi:10.1007/978-3-319-23108-2_5",
  abstract =     "Ensembles are groups of classifiers which cooperate in
                 order to reach a decision. Conventionally, the members
                 of an ensemble are trained sequentially, and typically
                 independently, and are not brought together until the
                 final stages of ensemble generation. In this paper, we
                 discuss the potential benefits of training classifiers
                 together, so that they learn to interact at an early
                 stage of their development. As a potential mechanism
                 for achieving this, we consider the biological concept
                 of mutualism, whereby cooperation emerges over the
                 course of biological evolution. We also discuss
                 potential mechanisms for implementing this approach
                 within an evolutionary algorithm context.",
  notes =        "Affiliated with School of Mathematical and Computer
                 Sciences, Heriot-Watt University",
}

Genetic Programming entries for Michael A Lones Stuart E Lacy Stephen L Smith

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