Evolving process-based models from psychological data using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Lane:2014:AISB,

• 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 performance.",
• notes = "Gives doi:10.13039/501100000269 for funding",

}

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

Citations