Introducing Semantic-Clustering Selection in Grammatical Evolution

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@InProceedings{Forstenlechner:2015:GECCOcomp,
  author =       "Stefan Forstenlechner and Miguel Nicolau and 
                 David Fagan and Michael O'Neill",
  title =        "Introducing Semantic-Clustering Selection in
                 Grammatical Evolution",
  booktitle =    "GECCO 2015 Semantic Methods in Genetic Programming
                 (SMGP'15) Workshop",
  year =         "2015",
  editor =       "Colin Johnson and Krzysztof Krawiec and 
                 Alberto Moraglio and Michael O'Neill",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Semantic Methods",
  pages =        "1277--1284",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2768502",
  DOI =          "doi:10.1145/2739482.2768502",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Semantics has gained much attention in the last few
                 years and new advanced crossover and mutation
                 operations have been created which use semantic
                 information to improve the quality and generalisability
                 of individuals in genetic programming. In this paper we
                 present a new selection operator in grammatical
                 evolution which uses semantic information of
                 individuals instead of just the fitness value. The
                 semantic traits of an individual are stored in a
                 vector. An unsupervised learning technique is used to
                 cluster individuals based on their semantic vector.
                 Individuals are only allowed to reproduce with
                 individuals from the same cluster to preserve semantic
                 locality and intensify the search in a certain semantic
                 area. At the same time, multiple semantic areas are
                 covered by the search as there exist multiple clusters
                 which cover different areas and therefore preserve
                 semantic diversity. This new selection operator is
                 tested on several symbolic regression benchmark
                 problems and compared to grammatical evolution with
                 tournament selection to analyse its performance.",
  notes =        "Also known as \cite{2768502} Distributed at
                 GECCO-2015.",
}

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

Citations