Evolvability in Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Eric Medvet and Fabio Daolio and Danny Tagliapietra",
  title =        "Evolvability in Grammatical Evolution",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "977--984",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071298",
  DOI =          "doi:10.1145/3071178.3071298",
  acmid =        "3071298",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, fitness-landscape, genotype-phenotype
                 mapping, locality",
  abstract =     "Evolvability is a measure of the ability of an
                 Evolutionary Algorithm (EA) to improve the fitness of
                 an individual when applying a genetic operator. Other
                 than the specific problem, many aspects of the EA may
                 impact on the evolvability most notably the genetic
                 operators and, if present, the genotype-phenotype
                 mapping function. Grammatical Evolution (GE) is an EA
                 in which the mapping function plays a crucial role
                 since it allows to map any binary genotype into a
                 program expressed in any user-provided language,
                 defined by a context-free grammar. While GE mapping
                 favoured a successful application of GE to many
                 different problems, it has also been criticized for
                 scarcely adhering to the variational inheritance
                 principle, which itself may hamper GE evolvability. In
                 this paper, we experimentally study GE evolvability in
                 different conditions, that is, problems, mapping
                 functions, genotype sizes, and genetic operators.
                 Results suggest that there is not a single factor
                 determining GE evolvability: in particular, the mapping
                 function alone does not deliver better evolvability
                 regardless of the problem. Instead, GE redundancy,
                 which itself is the result of the combined effect of
                 several factors, has a strong impact on the
  notes =        "Also known as \cite{Medvet:2017:EGE:3071178.3071298}
                 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 Fabio Daolio Danny Tagliapietra