Improving the Performance of CGPANN for Breast Cancer Diagnosis using Crossover and Radial Basis Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Timmy Manning and Paul Walsh",
  title =        "Improving the Performance of {CGPANN} for Breast
                 Cancer Diagnosis using Crossover and Radial Basis
  booktitle =    "11th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 {EvoBIO 2013}",
  year =         "2013",
  editor =       "Leonardo Vanneschi and William S. Bush and 
                 Mario Giacobini",
  month =        apr # " 3-5",
  series =       "LNCS",
  volume =       "7833",
  publisher =    "Springer Verlag",
  organisation = "EvoStar",
  address =      "Vienna, Austria",
  pages =        "165--176",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  isbn13 =       "978-3-642-37188-2",
  DOI =          "doi:10.1007/978-3-642-37189-9_15",
  abstract =     "Recently published evaluations of the topology and
                 weight evolving artificial neural network algorithm
                 Cartesian genetic programming evolved artificial neural
                 networks (CGPANN) have suggested it as a potentially
                 powerful tool for bioinformatics problems. In this
                 paper we provide an overview of the CGPANN algorithm
                 and a brief case study of its application to the
                 Wisconsin breast cancer diagnosis problem. Following
                 from this, we introduce and evaluate the use of RBF
                 kernels and crossover to CGPANN as a means of
                 increasing performance and consistency.",

Genetic Programming entries for Timmy Manning Paul J Walsh