Cost-benefit Analysis of Using Heuristics in ACGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Aleshunas:2011:CAoUHiA,
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title = "Cost-benefit Analysis of Using Heuristics in {ACGP}",
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author = "John Aleshunas and Cezary Janikow",
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pages = "1177--1183",
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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",
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DOI = "doi:10.1109/CEC.2011.5949749",
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abstract = "Constrained Genetic Programming (CGP) is a method of
searching the Genetic Programming search space
non-uniformly, giving preferences to certain subspaces
according to some heuristics. Adaptable CGP (ACGP) is a
method for discovery of the heuristics. CGP and ACGP
have previously demonstrated their capabilities using
first-order heuristics: parent-child probabilities.
Recently, the same advantage has been shown for
second-order heuristics: parent- children
probabilities. A natural question to ask is whether we
can benefit from extending ACGP with deeper-order
heuristics. This paper attempts to answer this question
by performing cost-benefit analysis while simulating
the higher- order heuristics environment. We show that
this method cannot be extended beyond the current
second or possibly third-order heuristics without a new
method to deal with the sheer number of such
deeper-order heuristics.",
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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
John Aleshunas
Cezary Z Janikow
Citations