An Empirical Study on the Accuracy of Computational Effort in Genetic Programming
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- @InProceedings{Barrero:2011:AESotAoCEiGP,
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title = "An Empirical Study on the Accuracy of Computational
Effort in Genetic Programming",
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author = "David F. Barrero and Maria R-Moreno and
Bonifacio Castano and David Camacho",
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pages = "1169--1176",
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booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
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year = "2011",
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editor = "Alice E. Smith",
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month = "5-8 " # jun,
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address = "New Orleans, USA",
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, computational
effort, estimation, variability sources, computational
complexity, estimation theory",
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DOI = "doi:10.1109/CEC.2011.5949748",
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abstract = "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 try to 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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notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
- }
Genetic Programming entries for
David F Barrero
Ma Dolores Rodriguez Moreno
Bonifacio Castano
David Camacho
Citations