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Using genetic programming to discover nonlinear variable interactions

  • Chris WestburyEmail author
  • Lori Buchanan
  • Michael Sanderson
  • Mijke Rhemtulla
  • Leah Phillips
Article
  • 279 Downloads

Abstract

Psychology has to deal with many interacting variables. The analyses usually used to uncover such relationships have many constraints that limit their utility. We briefly discuss these and describe recent work that uses genetic programming to evolve equations to combine variables in nonlinear ways in a number of different domains. We focus on four studies of interactions from lexical access experiments and psychometric problems. In all cases, genetic programming described nonlinear combinations of items in a manner that was subsequently independently verified. We discuss the general implications of genetic programming and related computational methods for multivariate problems in psychology.

Keywords

Fitness Function Genetic Programming Lexical Decision Turing Machine Lexical Access 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Psychonomic Society, Inc 2003

Authors and Affiliations

  • Chris Westbury
    • 1
    Email author
  • Lori Buchanan
    • 2
  • Michael Sanderson
    • 3
  • Mijke Rhemtulla
    • 3
  • Leah Phillips
    • 3
  1. 1.Department of PsychologyUniversity of AlbertaEdmontonCanada
  2. 2.University of WindsorWindsorCanada
  3. 3.University of AlbertaEdmontonCanada

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