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Evolving process-based models from psychological datausing genetic programming

Lane, Peter C. R., Sozou, Peter D., Addis, Mark and Gobet, Fernand (2014) Evolving process-based models from psychological datausing genetic programming. In: Proceedings of the AISB-50 Conference, 2014-04-01, London, United Kingdom, GBR.

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The development of computational models to provide explanations of psychological data can be achieved using semi-automated search techniques, such as genetic programming. One challenge with these techniques is to control the type of model that is evolved to be cognitively plausible – a typical problem is that of “bloating”, where continued evolution generates models of increasing size without improving overall fitness. In this paper we describe a system for representing psychological data, a class of process-based models, and algorithms for evolving models. We apply this system to the delayed match-to-sample task. We show how the challenge of bloating may be addressed by extending the fitness function to include measures of cognitive performance.

Item Type: Conference or Workshop Item (Paper)
Official URL:
Additional Information: © 2014 The Authors
Divisions: Philosophy, Logic and Scientific Method
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Date Deposited: 18 Apr 2016 15:20
Last Modified: 16 May 2024 11:09
Projects: ES/L003090/1
Funders: Economic and Social Research Council

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