Automatic Derivation of Search Objectives for Test-Based Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Krawiec:2015:EuroGP,
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author = "Krzysztof Krawiec and Pawel Liskowski",
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title = "Automatic Derivation of Search Objectives for
Test-Based Genetic Programming",
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booktitle = "18th European Conference on Genetic Programming",
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year = "2015",
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editor = "Penousal Machado and Malcolm I. Heywood and
James McDermott and Mauro Castelli and
Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
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series = "LNCS",
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volume = "9025",
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publisher = "Springer",
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pages = "53--65",
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address = "Copenhagen",
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month = "8-10 " # apr,
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, Program
synthesis, Test-based problems, Multiobjective
evolutionary computation",
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isbn13 = "978-3-319-16500-4",
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DOI = "doi:10.1007/978-3-319-16501-1_5",
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abstract = "In genetic programming (GP), programs are usually
evaluated by applying them to tests, and fitness
function indicates only how many of them have been
passed. We posit that scrutinising the outcomes of
programs interactions with individual tests may help
making program synthesis more effective. To this aim,
we propose DOC, a method that autonomously derives new
search objectives by clustering the outcomes of
interactions between programs in the population and the
tests. The derived objectives are subsequently used to
drive the selection process in a single or
multiobjective fashion. An extensive experimental
assessment on 15 discrete program synthesis tasks
representing two domains shows that DOC significantly
outperforms conventional GP and implicit fitness
sharing.",
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notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
conjunction with EvoCOP2015, EvoMusArt2015 and
EvoApplications2015",
- }
Genetic Programming entries for
Krzysztof Krawiec
Pawel Liskowski
Citations