title: Using genetic programming to discover nonlinear variable interactions.
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.
authors.affiliation: Department of Psychology, University of Alberta, Edmonton, Alberta, Canada. chrisw@ualberta.ca
authors.forename: Chris
authors.initials: C
authors.lastname: Westbury
authors.forename: Lori
authors.initials: L
authors.lastname: Buchanan
authors.forename: Michael
authors.initials: M
authors.lastname: Sanderson
authors.forename: Mijke
authors.initials: M
authors.lastname: Rhemtulla
authors.forename: Leah
authors.initials: L
authors.lastname: Phillips
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D005075:Biological Evolution; D003657:Decision Making; D006801:Humans; D007802:Language; D008957:Models, Genetic; D008960:Models, Psychological; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011584:Psychology / Q000379:methods*; D012107:Research Design; D012641:Selection, Genetic