Pareto, Population Partitioning, Price and Genetic Programming   [GP]

by

Langdon, W., B.

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Info: 1995
Keywords:Automatic Programming, Machine Learning, Genetic Programming, Genetic Algorithms, Artificial Evolution, Pareto fitness, Demes
Abstract:
A description of a use of Pareto optimality [PO] in genetic programming [GP] is given and an analogy with Genetic Algorithm fitness niches [GA] is drawn. Techniques to either spread the population across many pareto optimal fitness [PO] values or to reduce the spread are described. It is speculated that a wide spread may not aid Genetic Programming. [GP] 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. [FF]
Notes:
Submitted to AAAI Fall 1995 Genetic Programming Symposium
URL(s):Postscript
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BibTex:
@TechReport{langdon:1995:ppp,
  author =       "William B. Langdon",
  title =        "Pareto, Population Partitioning, Price and Genetic
                 Programming",
  institution =  "University College London",
  year =         "1995",
  type =         "Research Note",
  number =       "RN/95/29",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        apr,
  note =         "Submitted to AAAI Fall 1995 Genetic Programming
                 Symposium",
  keywords =     "Automatic Programming, Machine Learning, Genetic
                 Programming, Genetic Algorithms, Artificial Evolution,
                 Pareto fitness, Demes",
  URL =          "ftp://cs.ucl.ac.uk/genetic/papers/WBL_aaai-pppGP.ps",
  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.",
  size =         "11 pages",
}