Discovering predictive variables when evolving cognitive models

P.C.R. Lane; F. Gobet

doi:10.1007/11551188_12

Discovering predictive variables when evolving cognitive models

P.C.R. Lane, F. Gobet

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

35 Downloads (Pure)

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.

Original language	English
Pages (from-to)	108-117
Journal	Lecture Notes in Computer Science (LNCS)
Issue number	3rd Int Conf on Advances in Pattern Recognition
DOIs	https://doi.org/10.1007/11551188_12
Publication status	Published - 2005

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title = "Discovering predictive variables when evolving cognitive models",

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.",

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AB - 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.

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Discovering predictive variables when evolving cognitive models

Abstract

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