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Publication Detail
Pareto, Population Partitioning, Price and Genetic Programming
  • Publication Type:
  • Authors:
    Langdon WB
  • publication date:
  • Place of publication:
    Gower Street, London WC1E 6BT, UK
  • Report number:
  • Notes:
    keywords: genetic algorithms, genetic programming, Automatic Programming, Machine Learning, Artificial Evolution, Pareto fitness, Demes notes: Accepted by AAAI Fall 1995 Genetic Programming Symposium but withdrawn due to time pressures multiobjective Pareto front size: 11 pages
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.
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