An Effective Diversity Promotion Mechanism in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Medvet:2017:GECCOb,
  author =       "Eric Medvet and Alberto Bartoli and 
                 Giovanni Squillero",
  title =        "An Effective Diversity Promotion Mechanism in
                 Grammatical Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "247--248",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3076057",
  DOI =          "doi:10.1145/3067695.3076057",
  acmid =        "3076057",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, diversity, locality, performance,
                 representation",
  month =        "15-19 " # jul,
  abstract =     "Grammatical Evolution is an Evolutionary Algorithm
                 which can evolve programs in any language described by
                 a context-free grammar. A sequence of bits (the
                 genotype) is transformed into a string of the language
                 (the phenotype) by means of a mapping function, and
                 eventually into a fitness value. Unfortunately the
                 flexibility brought by the mapping is also likely to
                 introduce non-locality phenomena, reduce diversity, and
                 consequently hamper the effectiveness of the algorithm.
                 In this paper, we propose a novel technique for
                 promoting diversity, able to operate on three different
                 levels: genotype, phenotype, and fitness. The technique
                 is quite general, independent both from the specific
                 problem being tackled and from other components of the
                 evolutionary algorithm, such as genotype-phenotype
                 mapping, selection criteria, and genetic operators. We
                 experimentally demonstrate its efficacy in a wide range
                 of conditions and from different points of view. The
                 results also confirm the preponderant importance of the
                 phenotype-level analyses in diversity promotion.",
  notes =        "Also known as \cite{Medvet:2017:EDP:3067695.3076057}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Eric Medvet Alberto Bartoli Giovanni Squillero

Citations