Parameter Sweeps For Exploring GP Parameters

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Michael E. Samples and Jason M. Daida and 
                 Matt Byom and Matt Pizzimenti",
  title =        "Parameter Sweeps For Exploring {GP} Parameters",
  booktitle =    "Genetic and Evolutionary Computation Conference
                 {(GECCO2005)} workshop program",
  year =         "2005",
  month =        "25-29 " # jun,
  editor =       "Franz Rothlauf and Misty Blowers and 
                 J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and 
                 Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and 
                 Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and 
                 Claudio F. Lima and Xavier Llor{\`a} and 
                 Fernando Lobo and Laurence D. Merkle and Julian Miller and 
                 Jason H. Moore and Michael O'Neill and Martin Pelikan and 
                 Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and 
                 Stephen L. Smith and Hal Stringer and 
                 Keiki Takadama and Marc Toussaint and Stephen C. Upton and 
                 Alden H. Wright",
  publisher =    "ACM Press",
  address =      "Washington, D.C., USA",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "212--219",
  URL =          "",
  abstract =     "We describe our procedure and a software application
                 for conducting large parameter sweep experiments in
                 genetic and evolutionary computation research. Both
                 procedure and software allows a researcher to examine
                 multivariate nonlinearities that are common in genetic
                 and evolutionary computation. Experiments of this
                 nature are well suited to distributed computing
                 environments (such as Grids and clusters) and we
                 present an automated system for conducting parameter
                 sweep experiments on heterogeneous networks. Emphasis
                 is placed on experimental sampling, distributed
                 robustness, and data analysis. The parameter sweep
                 experimental procedure is easily applicable to any
                 experiment involving computer simulations but is
                 particularly well suited for evolutionary computation
  notes =        "Distributed on CD-ROM at GECCO-2005. ACM

Genetic Programming entries for Michael E Samples Jason M Daida Matthew J Byom Matt Pizzimenti