Information distance based fitness and diversity metrics

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Card:2010:geccocomp,
  author =       "Stuart W. Card",
  title =        "Information distance based fitness and diversity
                 metrics",
  booktitle =    "GECCO 2010 Entropy, information and complexity",
  year =         "2010",
  editor =       "Stuart William Card and Yossi Borenstein",
  isbn13 =       "978-1-4503-0073-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1851--1854",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830761.1830815",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Commensurate indicators of diversity and fitness with
                 desirable metric properties are derived from
                 information distances based on Shannon entropy and
                 Kolmogorov complexity. These metrics measure various
                 useful distances: from an information theoretic
                 characterisation of the phenotypic behaviour of a
                 candidate model in the population to that of an ideal
                 model of the target system's input-output relationship
                 (fitness); from behavior of one candidate model to that
                 of another (total information diversity); from the
                 information about the target provided by one model to
                 that provided by another (target relevant information
                 diversity); from the code of one model to that of
                 another (genotypic representation diversity); etc.
                 Algorithms are cited for calculating the Shannon
                 entropy based metrics from discrete data and estimating
                 analogs thereof from heuristically binned continuous
                 data; references are cited to methods for estimating
                 the Kolmogorov complexity based metric. Not in the
                 paper, but at the workshop, results will be shown of
                 applying these algorithms to several synthetic and real
                 world data sets: the simplest known deterministic
                 chaotic flow; symbolic regression test functions;
                 industrial process monitoring and control variables;
                 and international political leadership data. Ongoing
                 work is outlined.",
  notes =        "Also known as \cite{1830815} Distributed on CD-ROM at
                 GECCO-2010.

                 ACM Order Number 910102.",
}

Genetic Programming entries for Stu Card

Citations