Towards an understanding of locality in genetic programming

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@InProceedings{GalvanLopez:2010:gecco,
  author =       "Edgar Galvan-Lopez and James McDermott and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "Towards an understanding of locality in genetic
                 programming",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "901--908",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830646",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Locality - how well neighbouring genotypes correspond
                 to neighbouring phenotypes - has been defined as a key
                 element affecting how Evolutionary Computation systems
                 explore and exploit the search space. Locality has been
                 studied empirically using the typical Genetic Algorithm
                 (GA) representation (i.e., bitstrings), and it has been
                 argued that locality plays an important role in EC
                 performance. To our knowledge, there are few explicit
                 studies of locality using the typical Genetic
                 Programming (GP) representation (i.e., tree-like
                 structures). The aim of this paper is to address this
                 important research gap. We extend the
                 genotype-phenotype definition of locality to GP by
                 studying the relationship between genotypes and
                 fitness. We consider a mutation-based GP system applied
                 to two problems which are highly difficult to solve by
                 GP (a multimodal deceptive landscape and a highly
                 neutral landscape). To analyse in detail the locality
                 in these instances, we adopt three popular mutation
                 operators. We analyse the operators' genotypic step
                 sizes in terms of three distance measures taken from
                 the specialised literature and in terms of
                 corresponding fitness values. We also analyse the
                 frequencies of different sizes of fitness change.",
  notes =        "Also known as \cite{1830646} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",
}

Genetic Programming entries for Edgar Galvan Lopez James McDermott Michael O'Neill Anthony Brabazon

Citations