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ICAPR
2005
Springer

Discovering Predictive Variables When Evolving Cognitive Models

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Discovering Predictive Variables When Evolving Cognitive Models
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.
Peter C. R. Lane, Fernand Gobet
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ICAPR
Authors Peter C. R. Lane, Fernand Gobet
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