An empirical study on the accuracy of computational effort in Genetic Programming
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Institute of Electrical and Electronics EngineersDate
2011Citation
10.1109/CEC.2011.5949748
2011 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2011. 1164 – 1171
ISBN
978-1-4244-7834-7DOI
10.1109/CEC.2011.5949748Funded by
This work was partially supported by the MICYT project ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-6640Editor's Version
http://dx.doi.org/10.1109/CEC.2011.5949748Subjects
Computational complexity; Estimation theory; Genetic algorithms; InformáticaNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171Rights
© 2011 IEEEAbstract
Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.
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Google Scholar:Barrero, David F.
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R-Moreno, María Dolores
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Castaño, Bonifacio
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Camacho, David
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