Locality in Continuous Fitness-Valued Cases and Genetic Programming Difficulty

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Edgar Galvan and Leonardo Trujillo and 
                 James McDermott and Ahmed Kattan",
  title =        "Locality in Continuous Fitness-Valued Cases and
                 Genetic Programming Difficulty",
  booktitle =    "EVOLVE - A Bridge between Probability, Set Oriented
                 Numerics, and Evolutionary Computation {II}",
  year =         "2012",
  editor =       "Oliver Schuetze and Carlos A. {Coello Coello} and 
                 Alexandru-Adrian Tantar and Emilia Tantar and 
                 Pascal Bouvry and Pierre {Del Moral} and Pierrick Legrand",
  volume =       "175",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "41--56",
  address =      "Mexico City, Mexico",
  month =        aug # " 7-9",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-31519-0",
  DOI =          "doi:10.1007/978-3-642-31519-0_3",
  abstract =     "It is commonly accepted that a mapping is local if it
                 preserves neighbourhood. In Evolutionary Computation,
                 locality is generally described as the property that
                 neighbouring genotypes correspond to neighbouring
                 phenotypes. Locality has been classified in one of two
                 categories: high and low locality. It is said that a
                 representation has high locality if most genotypic
                 neighbours correspond to phenotypic neighbours. The
                 opposite is true for a representation that has low
                 locality. It is argued that a representation with high
                 locality performs better in evolutionary search
                 compared to a representation that has low locality. In
                 this work, we explore, for the first time, a study on
                 Genetic Programming (GP) locality in continuous fitness
                 valued cases. For this, we extended the original
                 definition of locality (first defined and used in
                 Genetic Algorithms using bitstrings) from
                 genotype-phenotype mapping to the genotype-fitness
                 mapping. Then, we defined three possible variants of
                 locality in GP regarding neighbourhood. The
                 experimental tests presented here use a set of symbolic
                 regression problems, two different encoding and two
                 different mutation operators. We show how locality can
                 be studied in this type of scenarios (continuous
                 fitness-valued cases) and that locality can
                 successfully been used as a performance prediction
  notes =        "EVOLVE-2012",
  affiliation =  "Distributed Systems Group, School of Computer Science
                 and Statistics, Trinity College, Dublin, Ireland",

Genetic Programming entries for Edgar Galvan Lopez Leonardo Trujillo James McDermott Ahmed Kattan