Using genetic programming to discover nonlinear variable interactions.

Full 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.

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Author information

Author/s: Westbury, Chris (C); Buchanan, Lori (L); Sanderson, Michael (M); Rhemtulla, Mijke (M); Phillips, Leah (L);

Affiliation: Department of Psychology, University of Alberta, Edmonton, Alberta, Canada. chrisw@ualberta.ca

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc (Behav Res Methods Instrum Comput), published in United States. (Language: eng)

Reference: 2003-May; vol 35 (issue 2) : pp 202-16

Dates: Created 2003/07/01; Completed 2003/08/29; Revised 2006/11/15;

PMID: 12834075, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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