Discovering predictive variables when evolving cognitive models
Lane, P.C.R. and Gobet, F.
(2005)
Discovering predictive variables when evolving cognitive models.
Lecture Notes in Computer Science (LNCS) (3rd In).
pp. 108-117.
ISSN 0302-9743
A non-dominated sorting genetic algorithm is used to evolve models of learning from di erent 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.
Item Type | Article |
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Additional information | “The original publication is available at www.springerlink.com”. Copyright Springer |
Date Deposited | 15 May 2025 11:36 |
Last Modified | 30 May 2025 23:33 |
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