A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{HuayangXie:2006:CEC,
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author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
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title = "A Study of Good Predecessor Programs for Reducing
Fitness Evaluation Cost in Genetic Programming",
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booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
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year = "2006",
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pages = "9211--9218",
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address = "Vancouver",
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month = "16-21 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7803-9487-9",
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DOI = "doi:10.1109/CEC.2006.1688641",
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size = "8 pages",
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abstract = "Good Predecessor Programs (GPPs) are the ancestors of
the best program found in a Genetic Programming (GP)
evolution. This paper reports on an investigation into
GPPs with the ultimate goal of reducing fitness
evaluation cost in tree-based GP systems. A framework
is developed for gathering information about GPPs and a
series of experiments is conducted on a symbolic
regression problem, a binary classification problem,
and a multi-class classification program with
increasing levels of difficulty in different domains.
The analysis of the data shows that during evolution,
GPPs typically constitute less than 33per cent of the
total programs evaluated, and may constitute less than
5per cent. The analysis results further shows that in
all evaluated programs, the proportion of GPPs is
reduced by increasing tournament size and to a less
extent, affected by population size. Problem difficulty
seems to have no clear influence on the proportion of
GPPs.",
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notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
",
- }
Genetic Programming entries for
Huayang Jason Xie
Mengjie Zhang
Peter Andreae
Citations