Too Much Knowledge Hurts: Acceleration of Genetic Programs for Learning Heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Frank Schmiedle and Daniel Grosse and 
                 Rolf Drechsler and Bernd Becker",
  title =        "Too Much Knowledge Hurts: Acceleration of Genetic
                 Programs for Learning Heuristics",
  booktitle =    "Computational Intelligence : Theory and Applications",
  year =         "2001",
  editor =       "Bernd Reusch",
  volume =       "2206",
  series =       "LNCS",
  pages =        "479--491",
  address =      "Dortmund, Germany",
  month =        "1-3 " # oct,
  organisation = "7th Fuzzy Days",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  ISBN =         "3-540-42732-5",
  size =         "pages",
  abstract =     "Among many other applications, evolutionary methods
                 have been used to develop heuristics for several
                 optimization problems in VLSI CAD in recent years.
                 Although learning is performed according to a set of
                 training benchmarks, it is most important to generate
                 heuristics that have a good generalization behaviour
                 and hence are well suited to be applied to unknown
                 examples. Besides large runtimes for learning, the
                 major drawback of these approaches is that they are
                 very sensitive to a variety of parameters for the
                 learning process.

                 In this paper, we study the impact of different
                 parameters, like e.g. stopping conditions, on the
                 quality of the results for learning heuristics for BDD
                 minimization. If learning takes too long, the developed
                 heuristics become too specific for the set of training
                 examples and in that case results of application to
                 unknown problem instances deteriorate. It will be
                 demonstrated here that runtime can be saved while even
                 improving the generalization behaviour of the
  notes =        "",

Genetic Programming entries for Frank Schmiedle Daniel Grosse Rolf Drechsler Bernd Becker