Evolutionary Partitioning Regression with Function Stacks

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Daniel A. Ashlock and Joseph Alexander Brown",
  title =        "Evolutionary Partitioning Regression with Function
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew Song Ong",
  pages =        "1469--1476",
  address =      "Vancouver",
  month =        "25-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743963",
  size =         "8 pages",
  abstract =     "Partitioning regression is the simultaneous fitting of
                 multiple models to a set of data and partitioning of
                 that data into easily modelled classes. The key to
                 partitioning regression with evolution is minimum error
                 assignment during fitness evaluation. Assigning a point
                 to the model for which it has the least error while
                 using evolution to minimize total model error
                 encourages the evolution of models that cleanly
                 partition data. This study demonstrates the efficacy of
                 partitioning regression with two or three models on
                 simple bivariate data sets. Possible generalizations to
                 the general case of clustering are outlined.",
  notes =        "CEC2016 WCCI2016",

Genetic Programming entries for Daniel Ashlock Joseph Alexander Brown