Using Evolvable Regressors to Partition Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Joseph A. Brown and Daniel Ashlock",
  title =        "Using Evolvable Regressors to Partition Data",
  booktitle =    "ANNIE 2010, Intelligent Engineering Systems through
                 Artificial Neural Networks",
  year =         "2010",
  editor =       "Cihan H. Dagli",
  volume =       "20",
  pages =        "187--194",
  address =      "St. Louis, Mo, USA",
  month =        nov # " 1-3",
  organisation = "Smart Engineering Systems Laboratory, Systems
                 Engineering Graduate Programs, Missouri University of
                 Science and Technology, 600 W. 14th St., Rolla, MO
                 65409 USA",
  publisher =    "ASME",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9780791859599",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1115/1.859599.paper24",
  abstract =     "This manuscript examines permitting multiple
                 populations of evolvable regressors to compete to be
                 the best model for the largest number of data points.
                 Competition between populations enables a natural
                 process of specialisation that implicitly partitions
                 the data. This partitioning technique uses
                 function-stack based regressors and has the ability to
                 discover the natural number of clusters in a data set
                 via a process of sub-population collapse.",
  notes =        "ASME Order Number: 859599",

Genetic Programming entries for Joseph Alexander Brown Daniel Ashlock