An efficient distance metric for linear genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Marco Gaudesi and Giovanni Squillero and 
                 Alberto Tonda",
  title =        "An efficient distance metric for linear genetic
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "925--932",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463495",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Defining a distance measure over the individuals in
                 the population of an Evolutionary Algorithm can be
                 exploited for several applications, ranging from
                 diversity preservation to balancing exploration and
                 exploitation. When individuals are encoded as strings
                 of bits or sets of real values, computing the distance
                 between any two can be a straightforward process; when
                 individuals are represented as trees or linear graphs,
                 however, quite often the user must resort to
                 phenotype-level problem-specific distance metrics. This
                 paper presents a generic genotype-level distance metric
                 for Linear Genetic Programming: the information
                 contained by an individual is represented as a set of
                 symbols, using n-grams to capture significant recurring
                 structures inside the genome. The difference in
                 information between two individuals is evaluated
                 resorting to a symmetric difference. Experimental
                 evaluations show that the proposed metric has a strong
                 correlation with phenotype-level problem-specific
                 distance measures in two problems where individuals
                 represent string of bits and Assembly-language
                 programs, respectively.",
  notes =        "Also known as \cite{2463495} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Marco Gaudesi Giovanni Squillero Alberto Tonda