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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
Publication 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:School of Social Sciences Research Papers
Psychology

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