Evolving Compact Solutions in Genetic Programming: A Case Study

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{blickle96,
  author =       "Tobias Blickle",
  title =        "Evolving Compact Solutions in Genetic Programming: A
                 Case Study",
  editor =       "Hans-Michael Voigt and Werner Ebeling and 
                 Ingo Rechenberg and Hans-Paul Schwefel",
  booktitle =    "Parallel Problem Solving From Nature IV. Proceedings
                 of the International Conference on Evolutionary
                 Computation",
  year =         "1996",
  publisher =    "Springer-Verlag",
  volume =       "1141",
  series =       "LNCS",
  pages =        "564--573",
  address =      "Berlin, Germany",
  publisher_address = "Heidelberg, Germany",
  month =        "22-26 " # sep,
  keywords =     "genetic algorithms, genetic programming, bloat,
                 deleting crossover",
  ISBN =         "3-540-61723-X",
  URL =          "http://www.handshake.de/user/blickle/publications/ppsn1.ps",
  URL =          "http://citeseer.ist.psu.edu/blickle96evolving.html",
  DOI =          "doi:10.1007/3-540-61723-X_1020",
  size =         "10 pages",
  abstract =     "Genetic programming (GP) is a variant of genetic
                 algorithms where the data structures handled are trees.
                 This makes GP especially useful for evolving functional
                 relationships or computer programs, as both can be
                 represented as trees. Symbolic regression is the
                 determination of a function dependence y=g ( x ) that
                 approximates a set of data points ( x i , y i ). In
                 this paper the feasibility of symbolic regression with
                 GP is demonstrated on two examples taken from different
                 domains. Furthermore several suggested methods from
                 literature are compared that are intended to improve GP
                 performance and the readability of solutions by taking
                 into account introns or redundancy that occurs in the
                 trees and keeping the size of the trees small. The
                 experiments show that GP is an elegant and useful tool
                 to derive complex functional dependencies on numerical
                 data.",
  notes =        "http://lautaro.fb10.tu-berlin.de/ppsniv.html
                 PPSN4

                 same as \cite{blickle:1996:ecs} Test of effectiveness
                 of GP, EDI, deleting and adaptive anti-bloat
                 techniques. Results differ continuous (symbolic
                 regression) v. discrete 6-mux deleting crossover
                 similar to code editing based on code interpretation
                 during fitness evaluation.",
  affiliation =  "Swiss Federal Institute of Technology (ETH) Computer
                 Engineering and Communication Networks Lab (TIK)
                 Gloriastrasse 35 8092 Zurich Switzerland Gloriastrasse
                 35 8092 Zurich Switzerland",
}

Genetic Programming entries for Tobias Blickle

Citations