Representations for Evolutionary Algorithms

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

  author =       "Franz Rothlauf",
  title =        "Representations for Evolutionary Algorithms",
  booktitle =    "GECCO 2015 Introductory Tutorials",
  year =         "2015",
  editor =       "Anabela Simoes",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, cartesian genetic programming",
  pages =        "345--366",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739482.2756593",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Successful and efficient use of evolutionary
                 algorithms (EA) depends on the choice of the genotype,
                 the problem representation (mapping from genotype to
                 phenotype) and on the choice of search operators that
                 are applied to the genotypes. These choices cannot be
                 made independently of each other. The question whether
                 a certain representation leads to better performing EAs
                 than an alternative representation can only be answered
                 when the operators applied are taken into
                 consideration. The reverse is also true: deciding
                 between alternative operators is only meaningful for a
                 given representation.

                 In EA practice one can distinguish two complementary
                 approaches. The first approach uses indirect
                 representations where a solution is encoded in a
                 standard data structure, such as strings, vectors, or
                 discrete permutations, and standard off-the-shelf
                 search operators are applied to these genotypes. This
                 is for example the case in standard genetic algorithms,
                 evolution strategies, and some genetic programming
                 approaches like grammatical evolution or Cartesian
                 genetic programming. To evaluate the solution, the
                 genotype needs to be mapped to the phenotype space. The
                 proper choice of this genotype-phenotype mapping is
                 important for the performance of the EA search process.
                 The second approach, the direct representation, encodes
                 solutions to the problem in its most natural space and
                 designs search operators to operate on this

                 Research in the last few years has identified a number
                 of key concepts to analyse the influence of
                 representation-operator combinations on EA performance.
                 Relevant properties of representations are locality and

                 Locality is a result of the interplay between the
                 search operator and the genotype-phenotype mapping.
                 Representations are redundant if the number of
                 phenotypes exceeds the number of possible genotypes.
                 Redundant representations can lead to biased encodings
                 if some phenotypes are on average represented by a
                 larger number of genotypes or search operators favour
                 some kind of phenotypes.

                 The tutorial gives a brief overview about existing
                 guidelines for representation design, illustrates the
                 different aspects of representations, gives a brief
                 overview of models describing the different aspects,
                 and illustrates the relevance of the aspects with
                 practical examples.

                 It is expected that the participants have a basic
                 understanding of EA principles.",
  notes =        "Also known as \cite{2756593} Distributed at

Genetic Programming entries for Franz Rothlauf