Fitness landscapes and problem hardness in genetic programming

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

@InProceedings{Vanneschi:2010:geccocomp,
  author =       "Leonardo Vanneschi",
  title =        "Fitness landscapes and problem hardness in genetic
                 programming",
  booktitle =    "GECCO 2010 Specialized techniques and applications
                 tutorials",
  year =         "2010",
  editor =       "Una-May O'Reilly",
  isbn13 =       "978-1-4503-0073-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "2711--2738",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830761.1830916",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The performance of searching agents, or
                 metaheuristics, like evolutionary algorithms (genetics
                 algorithms, genetic programming, etc.) or local search
                 algorithms (simulated annealing, tabu search, etc.)
                 depend on some properties of the search space
                 structure. One concept that allows us to analyse the
                 search space is the fitness landscape. In the case of
                 Genetic Programming, defining and handling fitness
                 landscapes is a particularly hard task, given the
                 complexity of the structures being evolved of the
                 genetic operators used. This tutorial presents some
                 general definitions of fitness landscape. Subsequently,
                 we will try to instantiate the concept of fitness
                 landscape to Genetic Programming, discussing problems.
                 The concept of landscape geometry will be introduced
                 and some of the most common landscape geometries and
                 the dynamics of Genetic Programming on those landscapes
                 will be discussed. After that, the binding between
                 fitness landscapes and problem difficulty will be
                 discussed and a set of measures that characterise the
                 difficulty of a metaheuristic in searching solutions in
                 a fitness landscape are analysed. Among those measures,
                 particular relevance will be given to Fitness Distance
                 Correlation (FDC), Negative Slope Coefficient (NSC), a
                 set of measures bound to the concept of Neutrality and
                 some distance metrics and/or similarity measures that
                 are consistent with the most commonly used genetic
                 operators (in particular the recently defined subtree
                 crossover based distance). Finally, some open questions
                 about fitness landscapes are discussed.",
  notes =        "Also known as \cite{1830916} Distributed on CD-ROM at
                 GECCO-2010.

                 ACM Order Number 910102.",
}

Genetic Programming entries for Leonardo Vanneschi

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