Comparing Genetic Programming and Evolution Strategies on Inferring Gene Regulatory Networks

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

@InProceedings{streichert:cgp:gecco2004,
  author =       "Felix Streichert and Hannes Planatscher and 
                 Christian Spieth and Holger Ulmer and Andreas Zell",
  title =        "Comparing Genetic Programming and Evolution Strategies
                 on Inferring Gene Regulatory Networks",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part I",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "471--480",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3102",
  ISBN =         "3-540-22344-4",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b98643",
  URL =          "http://www-ra.informatik.uni-tuebingen.de/publikationen/2004/streichert04comparing.pdf",
  size =         "10",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In recent years several strategies for inferring gene
                 regulatory networks from observed time series data of
                 gene expression have been suggested based on
                 Evolutionary Algorithms. But often only few problem
                 instances are investigated and the proposed strategies
                 are rarely compared to alternative strategies. In this
                 paper we compare Evolution Strategies and Genetic
                 Programming with respect to their performance on
                 multiple problem instances with varying parameters. We
                 show that single problem instances are not sufficient
                 to prove the effectiveness of a given strategy and that
                 the Genetic Programming approach is less prone to
                 varying instances than the Evolution Strategy.",
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)",
}

Genetic Programming entries for Felix Streichert Hannes Planatscher Christian Spieth Holger Ulmer Andreas Zell

Citations