Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/775
Title: Discovering predictive variables when evolving cognitive models
Authors: Lane, PCR
Gobet, F
Keywords: Genetic algorithm;Non-dominated;Model;Theory development;Computational modelling;Concept formation;CHREST;Connectionism
Issue Date: 2005
Publisher: Third International Conference on Advances in Pattern Recognition.
Citation: Third International Conference on Advances in Pattern Recognition
Abstract: A non-dominated sorting genetic algorithm is used to evolve models of learning from different theories for multiple tasks. Correlation analysis is performed to identify parameters which affect performance on specific tasks; these are the predictive variables. Mutation is biased so that changes to parameter values tend to preserve values within the population's current range. Experimental results show that optimal models are evolved, and also that uncovering predictive variables is beneficial in improving the rate of convergence.
URI: http://bura.brunel.ac.uk/handle/2438/775
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

Files in This Item:
File Description SizeFormat 
lane-icapr05.pdf128.12 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.