Heuristic Learning based on Genetic Programming

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

@InProceedings{drechsler:2001:EuroGP,
  author =       "Nicole Drechsler and Frank Schmiedle and 
                 Daniel Grosse and Rolf Drechsler",
  title =        "Heuristic Learning based on Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "1--10",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Heuristic
                 Learning, VLSI CAD, BDD, Binary Decision Diagrams",
  ISBN =         "3-540-41899-7",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=1",
  DOI =          "doi:10.1007/3-540-45355-5_1",
  size =         "10 pages",
  abstract =     "In this paper we present an approach to learning
                 heuristics based on Genetic Programming (GP). Instead
                 of directly solving the problem by application of GP,
                 GP is used to develop a heuristic that is applied to
                 the problem instance. By this, the typical large
                 runtimes of evolutionary methods have to be invested
                 only once in the learning phase. The resulting
                 heuristic is very fast. The technique is applied to a
                 field from the area of VLSI CAD, i.e. minimization of
                 Binary Decision Diagrams (BDDs). We chose this topic
                 due to its high practical relevance and since it
                 matches the criteria where our algorithm works best,
                 i.e. large problem instances where standard
                 evolutionary techniques cannot be applied due to their
                 large runtimes. Our experiments show that we obtain
                 high quality results that outperform previous methods,
                 while keeping the advantage of low runtimes.",
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp}",
}

Genetic Programming entries for Nicole Drechsler Frank Schmiedle Daniel Grosse Rolf Drechsler

Citations