Semantics-Based Crossover for Program Synthesis in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Stefan Forstenlechner and David Fagan and 
                 Miguel Nicolau and Michael O'Neill",
  title =        "Semantics-Based Crossover for Program Synthesis in
                 Genetic Programming",
  booktitle =    "Artificial Evolution, EA-2017",
  year =         "2017",
  editor =       "Evelyne Lutton and Pierrick Legrand and 
                 Pierre Parrend and Nicolas Monmarche and Marc Schoenauer",
  volume =       "10764",
  series =       "LNCS",
  pages =        "58--71",
  address =      "Paris, France",
  month =        oct # " 25-27",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming, Crossover",
  isbn13 =       "978-3-319-78133-4",
  DOI =          "doi:10.1007/978-3-319-78133-4_5",
  size =         "14 pages",
  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",
  notes =        "Published 2018",

Genetic Programming entries for Stefan Forstenlechner David Fagan Miguel Nicolau Michael O'Neill