Comparison of Semantic-aware Selection Methods in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Liskowski:2015:GECCOcomp,
  author =       "Pawel Liskowski and Krzysztof Krawiec and 
                 Thomas Helmuth and Lee Spector",
  title =        "Comparison of Semantic-aware Selection Methods in
                 Genetic Programming",
  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, Semantic
                 Methods in (SMGP'15) Workshop",
  pages =        "1301--1307",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2768505",
  DOI =          "doi:10.1145/2739482.2768505",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This study investigates the performance of several
                 semantic-aware selection methods for genetic
                 programming (GP). In particular, we consider methods
                 that do not rely on complete GP semantics (i.e., a
                 tuple of outputs produced by a program for fitness
                 cases (tests)), but on binary outcome vectors that only
                 state whether a given test has been passed by a program
                 or not. This allows us to relate to test-based problems
                 commonly considered in the domain of coevolutionary
                 algorithms and, in prospect, to address a wider range
                 of practical problems, in particular the problems where
                 desired program output is unknown (e.g., evolving GP
                 controllers). The selection methods considered in the
                 paper include implicit fitness sharing (ifs), discovery
                 of derived objectives (doc), lexicase selection (lex),
                 as well as a hybrid of the latter two. These
                 techniques, together with a few variants, are
                 experimentally compared to each other and to
                 conventional GP on a battery of discrete benchmark
                 problems. The outcomes indicate superior performance of
                 lex and ifs, with some variants of doc showing certain
                 potential.",
  notes =        "Also known as \cite{2768505} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec Thomas Helmuth Lee Spector

Citations