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 2011 Late breaking abstracts",
  year =         "2011",
  editor =       "Christian Blum",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1191--1212",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002132",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Successful and efficient use of evolutionary
                 algorithms (EAs) 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. 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.
                 These concepts are *locality and *redundancy.

                 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.
                 Furthermore, redundant representations can lead to
                 biased encodings if some phenotypes are on average
                 represented by a larger number of genotypes. Finally, a
                 bias need not be the result of the representation but
                 can also be caused by the search operator.

                 The tutorial gives a brief overview about existing
                 guidelines for representation design, illustrates the
                 different aspects of representations, gives a brief
                 overview of theoretical 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{2002132} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Franz Rothlauf