An Analysis of Koza's Computational Effort Statistic for Genetic Programming
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- @InProceedings{christensen:2002:EuroGP,
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title = "An Analysis of {Koza}'s Computational Effort Statistic
for Genetic Programming",
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author = "Steffen Christensen and Franz Oppacher",
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editor = "James A. Foster and Evelyne Lutton and
Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
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booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
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year = "2002",
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volume = "2278",
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series = "LNCS",
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pages = "182--191",
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publisher = "Springer-Verlag",
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address = "Kinsale, Ireland",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-43378-3",
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DOI = "doi:10.1007/3-540-45984-7_18",
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abstract = "As research into the theory of genetic programming
progresses, more effort is being placed on
systematically comparing results to give an indication
of the effectiveness of sundry modifications to
traditional GP. The statistic that is commonly used to
report the amount of computational effort to solve a
particular problem with 99percent probability is Koza's
I(M, i, z) statistic. This paper analyzes this measure
from a statistical perspective. In particular, Koza's I
tends to underestimate the true computational effort,
by 25percent or more for commonly used GP parameters
and run sizes. The magnitude of this underestimate is
nonlinearly decreasing with increasing run count,
leading to the possibility that published results based
on few runs may in fact be unmatchable when replicated
at higher resolution. Additional analysis shows that
this statistic also under reports the generation at
which optimal results are achieved.",
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notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
- }
Genetic Programming entries for
Steffen Christensen
Franz Oppacher
Citations