General Schema Theory for Genetic Programming with Subtree-Swapping Crossover

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

@TechReport{Poli00-16,
  author =       "Riccardo Poli",
  title =        "General Schema Theory for Genetic Programming with
                 Subtree-Swapping Crossover",
  institution =  "University of Birmingham, School of Computer Science",
  number =       "CSRP-00-16",
  month =        nov,
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  email =        "R.Poli@cs.bham.ac.uk",
  file =         "/2000/CSRP-00-16.ps.gz",
  URL =          "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-16.ps.gz",
  abstract =     "In this paper a new general schema theory for genetic
                 programming is presented. Like other recent GP schema
                 theory results, the theory gives an exact formulation
                 (rather than a lower bound) for the expected number of
                 instances of a schema at the next generation. The
                 theory is based on a Cartesian node reference system
                 which makes it possible to describe programs as
                 functions over the space N^2 and allows one to model
                 the process of selection of the crossover points of
                 subtree-swapping crossovers as a probability
                 distribution over N^4. The theory is also based on the
                 notion of variable-arity hyperschema, which generalises
                 previous definitions of schema or hyperschema
                 introduced in GP. The theory includes two main theorems
                 describing the propagation of GP schemata: a
                 microscopic schema theorem and a macroscopic one. The
                 microscopic version is applicable to crossover
                 operators which replace a subtree in one parent with a
                 subtree from the other parent to produce the offspring.
                 Therefore, this theorem is equally applicable to
                 standard GP crossover with and without uniform
                 selection of the crossover points, as it is to
                 one-point crossover, size-fair crossover,
                 strongly-typed GP crossover, context-preserving
                 crossover and many others. The macroscopic version is
                 applicable to crossover operators in which the
                 probability of selecting any two crossover points in
                 the parents depends only on their size and shape. In
                 the paper we provide examples which show how the theory
                 can be specialised to specific crossover operators and
                 how it can be used to derive other general results such
                 as an exact definition of effective fitness and a
                 size-evolution equation for GP with subtree-swapping
                 crossover.",
}

Genetic Programming entries for Riccardo Poli

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