On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  title =        "On the Success Rate of Crossover Operators for Genetic
                 Programming with Offspring Selection",
  author =       "Gabriel Kronberger and Stephan M. Winkler and 
                 Michael Affenzeller and Andreas Beham and Stefan Wagner",
  booktitle =    "12th International Conference on Computer Aided
                 Systems Theory, EUROCAST 2009",
  year =         "2009",
  volume =       "5717",
  series =       "Lecture Notes in Computer Science",
  pages =        "793--800",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb # " 15-20",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-04771-8",
  DOI =          "doi:10.1007/978-3-642-04772-5_102",
  bibdate =      "2009-12-15",
  bibsource =    "DBLP,
  abstract =     "Genetic programming is a powerful heuristic search
                 technique that is used for a number of real world
                 applications to solve amongst others regression,
                 classification, and time-series forecasting problems. A
                 lot of progress towards a theoretic description of
                 genetic programming in form of schema theorems has been
                 made, but the internal dynamics and success factors of
                 genetic programming are still not fully understood. In
                 particular, the effects of different crossover
                 operators in combination with offspring selection are
                 largely unknown.

                 This contribution sheds light on the ability of
                 well-known GP crossover operators to create better
                 offspring when applied to benchmark problems. We
                 conclude that standard (sub-tree swapping) crossover is
                 a good default choice in combination with offspring
                 selection, and that GP with offspring selection and
                 random selection of crossover operators can improve the
                 performance of the algorithm in terms of best solution
                 quality when no solution size constraints are
  notes =        "Acknowledgment G.K. thanks the participants of EuroGP
                 2009 for the insightful comments and discussions.

Genetic Programming entries for Gabriel Kronberger Stephan M Winkler Michael Affenzeller Andreas Beham Stefan Wagner