Evolving process-based models from psychological data using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Peter C. R. Lane and Peter D. Sozou and Mark Addis and 
                 Fernand Gobet",
  title =        "Evolving process-based models from psychological data
                 using genetic programming",
  booktitle =    "Proceedings of the AISB-50 Conference",
  year =         "2014",
  address =      "Goldsmiths, University of London",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://eprints.lse.ac.uk/66170/",
  URL =          "http://eprints.lse.ac.uk/66170/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_Content_Sozou%2CP.D_Sozou_Evolving_Process-Based_Models.pdf",
  size =         "6 pages",
  abstract =     "The development of computational models to provide
                 explanations of psychological data can be achieved
                 using semi-automated search techniques, such as genetic
                 programming. One challenge with these techniques is to
                 control the type of model that is evolved to be
                 cognitively plausible – a typical problem is that of
                 bloating, where continued evolution generates models of
                 increasing size without improving overall fitness. In
                 this paper we describe a system for representing
                 psychological data, a class of process-based models,
                 and algorithms for evolving models. We apply this
                 system to the delayed-match-to-sample task. We show how
                 the challenge of bloating may be addressed by extending
                 the fitness function to include measures of cognitive
  notes =        "Gives doi:10.13039/501100000269 for funding",

Genetic Programming entries for Peter C R Lane Peter D Sozou Mark Addis Fernand Gobet