Pareto, Population Partitioning, Price and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "W. B. Langdon",
  title =        "Pareto, Population Partitioning, Price and Genetic
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/29",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Automatic
                 Programming, Machine Learning, Artificial Evolution,
                 Pareto fitness, Demes",
  URL =          "",
  URL =          "",
  abstract =     "A description of a use of Pareto optimality in genetic
                 programming is given and an analogy with Genetic
                 Algorithm fitness niches is drawn. Techniques to either
                 spread the population across many pareto optimal
                 fitness values or to reduce the spread are described.
                 It is speculated that a wide spread may not aid Genetic
                 Programming. It is suggested that this might give
                 useful insight into many GPs whose fitness is composed
                 of several sub-objectives.

                 The successful use of demic populations in GP leads to
                 speculation that smaller evolutionary steps might aid
                 GP in the long run.

                 An example is given where Price's covariance theorem
                 helped when designing a GP fitness function.",
  notes =        "Accepted by AAAI Fall 1995 Genetic Programming
                 Symposium but withdrawn due to time

                 multiobjective Pareto front",
  size =         "11 pages",

Genetic Programming entries for William B Langdon