Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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://gpbib.cs.ucl.ac.uk/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

Citations