Evolving reusable 3D packing heuristics with genetic programming

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@InProceedings{DBLP:conf/gecco/AllenBHK09,
  author =       "Sam Allen and Edmund K. Burke and Matthew R. Hyde and 
                 Graham Kendall",
  title =        "Evolving reusable {3D} packing heuristics with genetic
                 programming",
  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 =        "931--938",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570029",
  abstract =     "This paper compares the quality of reusable heuristics
                 designed by genetic programming (GP) to those designed
                 by human programmers. The heuristics are designed for
                 the three dimensional knapsack packing problem.
                 Evolutionary computation has been employed many times
                 to search for good quality solutions to such problems.
                 However, actually designing heuristics with GP for this
                 problem domain has never been investigated before. In
                 contrast, the literature shows that it has taken years
                 of experience by human analysts to design the very
                 effective heuristic methods that currently
                 exist.

                 Hyper-heuristics search a space of heuristics, rather
                 than directly searching a solution space. GP operates
                 as a hyper-heuristic in this paper, because it searches
                 the space of heuristics that can be constructed from a
                 given set of components. We show that GP can design
                 simple, yet effective, stand-alone constructive
                 heuristics. While these heuristics do not represent the
                 best in the literature, the fact that they are designed
                 by evolutionary computation, and are human competitive,
                 provides evidence that further improvements in this GP
                 methodology could yield heuristics superior to those
                 designed by humans.",
  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 Sam D Allen Edmund Burke Matthew R Hyde Graham Kendall

Citations