Semantics-Based Crossover for Program Synthesis in Genetic Programming
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- @InProceedings{Forstenlechner:2017:EA,
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author = "Stefan Forstenlechner and David Fagan and
Miguel Nicolau and Michael O'Neill",
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title = "Semantics-Based Crossover for Program Synthesis in
Genetic Programming",
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booktitle = "Artificial Evolution, EA-2017",
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year = "2017",
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editor = "Evelyne Lutton and Pierrick Legrand and
Pierre Parrend and Nicolas Monmarche and Marc Schoenauer",
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volume = "10764",
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series = "LNCS",
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pages = "58--71",
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address = "Paris, France",
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month = oct # " 25-27",
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publisher = "Springer",
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note = "Revised Selected Papers",
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keywords = "genetic algorithms, genetic programming, Crossover",
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isbn13 = "978-3-319-78133-4",
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DOI = "doi:10.1007/978-3-319-78133-4_5",
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size = "14 pages",
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abstract = "Semantic information has been used to create operators
that improve performance in genetic programming. As
different problem domains have different semantics,
extracting semantics and calculating semantic
similarity is of tantamount importance to use semantic
operators for each domain. To date researchers have
struggled to effectively do this beyond the Boolean and
regression problem domain. In this paper, a semantic
similarity-based crossover is tested in the problem
domain of program synthesis. For this purpose, a
similarity measure based on the execution trace of a
program is introduced. Subtree crossover as well as
semantic similarity-based crossover are analysed on
performance and semantic aspects. The goal is to
introduce the Semantic Similarity-based Crossover in
the program synthesis domain and to study the effects
of using semantic locality. The results show that
semantic crossover produces more semantically different
children as well as more children that are better than
their parents compared to subtree crossover",
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notes = "Published 2018",
- }
Genetic Programming entries for
Stefan Forstenlechner
David Fagan
Miguel Nicolau
Michael O'Neill
Citations