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.
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Lane, P.C.R., Gobet, F. (2005). Discovering Predictive Variables When Evolving Cognitive Models. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_12
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DOI: https://doi.org/10.1007/11551188_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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