A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming
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- @TechReport{vuw-CS-TR-06-3,
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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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institution = "Computer Science, Victoria University of Wellington",
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year = "2006",
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number = "CS-TR-06-3",
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address = "New Zealand",
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keywords = "genetic algorithms, genetic programming, Fitness
evaluation, good predecessor programs, population
clustering",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-3.abs.html",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-3.pdf",
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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 between 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.",
- }
Genetic Programming entries for
Huayang Jason Xie
Mengjie Zhang
Peter Andreae
Citations