Semantic tournament selection for genetic programming based on statistical analysis of error vectors
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- @Article{Chu:2018:IS,
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author = "Thi Houng Chu and Quang Uy Nguyen and
Michael O'Neill",
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title = "Semantic tournament selection for genetic programming
based on statistical analysis of error vectors",
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journal = "Information Sciences",
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year = "2018",
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volume = "436-437",
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pages = "352--366",
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month = apr,
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keywords = "genetic algorithms, genetic programming Tournament
selection, Statistical test, Code bloat, Semantics",
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DOI = "doi:10.1016/j.ins.2018.01.030",
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size = "15 pages",
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abstract = "The selection mechanism plays a very important role in
the performance of Genetic Programming (GP). Among
several selection techniques, tournament selection is
often considered the most popular. Standard tournament
selection randomly selects a set of individuals from
the population and the individual with the best fitness
value is chosen as the winner. However, an opportunity
exists to enhance tournament selection as the standard
approach ignores finer-grained semantics which can be
collected during GP program execution. In the case of
symbolic regression problems, the error vectors on the
training fitness cases can be used in a more detailed
quantitative comparison. In this paper we introduce the
use of a statistical test into GP tournament selection
that uses information from the individual's error
vector, and three variants of the selection strategy
are proposed. We tested these methods on twenty five
regression problems and their noisy variants. The
experimental results demonstrate the benefit of the
proposed methods in reducing GP code growth and
improving the generalisation behaviour of GP solutions
when compared to standard tournament selection, a
similar selection technique and a state of the art
bloat control approach.",
- }
Genetic Programming entries for
Thi Houng Chu
Quang Uy Nguyen
Michael O'Neill
Citations