Predicting solution rank to improve performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Michael D. Schmidt and Hod Lipson",
  title =        "Predicting solution rank to improve performance",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "949--956",
  keywords =     "genetic algorithms, genetic programming, coevolution,
                 symbolic regression",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830652",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Many applications of evolutionary algorithms use
                 fitness approximations, for example coarse-grained
                 simulations in lieu of computationally intensive
                 simulations. Here, we propose that it is better to
                 learn approximations that accurately predict the ranks
                 of individuals rather than explicitly estimating their
                 real-valued fitness values. We present an algorithm
                 that coevolves a rank-predictor which optimises to
                 accurately rank the evolving solution population. We
                 compare this method with a similar algorithm that uses
                 fitness-predictors to approximate real-valued
                 fitnesses. We benchmark the two approaches using
                 thousands of randomly-generated test problems in
                 Symbolic Regression with varying difficulties. The rank
                 prediction method showed a 5-fold reduction in
                 computational effort for similar convergence rates.
                 Rank prediction also produced less bloated solutions
                 than fitness prediction.",
  notes =        "Randomly generated symbolic regression problems.
                 Co-evolve three populations. Sine Cosine. Bloat

                 See also \cite{Schmidt:2010:GPTP}. Also known as
                 \cite{1830652} GECCO-2010 A joint meeting of the
                 nineteenth international conference on genetic
                 algorithms (ICGA-2010) and the fifteenth annual genetic
                 programming conference (GP-2010)",

Genetic Programming entries for Michael D Schmidt Hod Lipson