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        Discovering predictive variables when evolving cognitive models

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        902198.pdf (PDF, 128Kb)
        Author
        Lane, P.C.R.
        Gobet, F.
        Attention
        2299/5575
        Abstract
        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.
        Publication date
        2005
        Published in
        Lecture Notes in Computer Science
        Published version
        https://doi.org/10.1007/11551188_12
        Other links
        http://hdl.handle.net/2299/5575
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