Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1068286,
  author =       "Emily M. Zechman and S. Ranji Ranjithan",
  title =        "Multipopulation cooperative coevolutionary programming
                 (MCCP) to enhance design innovation",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "2",
  ISBN =         "1-59593-010-8",
  pages =        "1641--1648",
  address =      "Washington DC, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1641.pdf",
  DOI =          "doi:10.1145/1068009.1068286",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, design,
                 evolutionary programming, lymphoma cancer
                 classification, niching",
  abstract =     "the development of an evolutionary algorithm called
                 Multipopulation Cooperative Coevolutionary Programming
                 (MCCP) that extends Genetic Programming (GP) to search
                 for a set of maximally different solutions for program
                 induction problems. The GP search is structured to
                 generate a set of alternatives that are similar in
                 design performance, but are dissimilar from each other
                 in the solution (or design parameter) space. This is
                 expected to yield potentially more creative designs,
                 thus enhancing design innovation. Application of MCCP
                 is demonstrated through an illustrative example
                 involving GP-based classification of genetic data to
                 diagnose malignancy in cancer. Four different
                 classifiers, based on highly dissimilar combinations of
                 genes, but with similar prediction performances were
                 generated. As these classifiers use a diverse set of
                 genes, they are collectively more effective in
                 screening cancer samples that may not all properly
                 express every gene.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052",
}

Genetic Programming entries for Emily M Zechman S Ranji Ranjithan

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