Tournament Selection based on Statistical Test in Genetic Programming
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Chu:2016:PPSN,
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author = "Thi Huong Chu and Quang Uy Nguyen and
Michael O'Neill",
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title = "Tournament Selection based on Statistical Test in
Genetic Programming",
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booktitle = "14th International Conference on Parallel Problem
Solving from Nature",
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year = "2016",
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editor = "Julia Handl and Emma Hart and Peter R. Lewis and
Manuel Lopez-Ibanez and Gabriela Ochoa and
Ben Paechter",
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volume = "9921",
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series = "LNCS",
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pages = "303--312",
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address = "Edinburgh",
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month = "17-21 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-45823-6",
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DOI = "doi:10.1007/978-3-319-45823-6_28",
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abstract = "Selection plays a critical role in the performance of
evolutionary algorithms. Tournament selection is often
considered the most popular techniques among several
selection methods. Standard tournament selection
randomly selects several individuals from the
population and the individual with the best fitness
value is chosen as the winner. In the context of
Genetic Programming, this approach ignores the error
value on the fitness cases of the problem emphasising
relative fitness quality rather than detailed
quantitative comparison. Subsequently, potentially
useful information from the error vector may be lost.
In this paper, we introduce the use of a statistical
test into selection that uses information from the
individual's error vector. Two variants of tournament
selection are proposed, and tested on Genetic
Programming for symbolic regression problems. On the
benchmark problems examined we observe a benefit of the
proposed methods in reducing code growth and
generalisation error.",
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notes = "PPSN2016 Faculty of IT, Le Quy Don Technical
University, Hanoi, Vietnam",
- }
Genetic Programming entries for
Thi Huong Chu
Quang Uy Nguyen
Michael O'Neill
Citations