Scalability, generalization and coevolution -- experimental comparisons applied to automated facility layout planning

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@InProceedings{DBLP:conf/gecco/FuruholmenGHT09,
  author =       "Marcus Furuholmen and Kyrre Harald Glette and 
                 Mats Erling Hovin and Jim Torresen",
  title =        "Scalability, generalization and coevolution --
                 experimental comparisons applied to automated facility
                 layout planning",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "691--698",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1569997",
  abstract =     "Several practical problems in industry are difficult
                 to optimize, both in terms of scalability and
                 representation. Heuristics designed by domain experts
                 are frequently applied to such problems. However,
                 designing optimized heuristics can be a non-trivial
                 task. One such difficult problem is the Facility Layout
                 Problem (FLP) which is concerned with the allocation of
                 activities to space. This paper is concerned with the
                 block layout problem, where the activities require a
                 fixed size and shape (modules). This problem is
                 commonly divided into two sub problems; one of creating
                 an initial feasible layout and one of improving the
                 layout by interchanging the location of activities. We
                 investigate how to extract novel heuristics for the FLP
                 by applying an approach called Cooperative
                 Coevolutionary Gene Expression Programming (CCGEP). By
                 taking advantage of the natural problem decomposition,
                 one species evolves heuristics for pre-scheduling, and
                 another for allocating the activities onto the plant.
                 An experimental, comparative approach investigates
                 various features of the CCGEP approach. The results
                 show that the evolved heuristics converge to suboptimal
                 solutions as the problem size grows. However,
                 coevolution has a positive effect on optimization of
                 single problem instances. Expensive fitness evaluations
                 may be limited by evolving generalized heuristics
                 applicable to unseen fitness cases of arbitrary
                 sizes.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Marcus Furuholmen Kyrre Harald Glette Mats Erling Hovin Jim Torresen

Citations