Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{1277273,
  author =       "Edmund K. Burke and Matthew R. Hyde and 
                 Graham Kendall and John Woodward",
  title =        "Automatic heuristic generation with genetic
                 programming: evolving a jack-of-all-trades or a master
                 of one",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1559--1565",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1559.pdf",
  DOI =          "doi:10.1145/1276958.1277273",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, bin packing,
                 heuristics, hyper heuristic, reliability",
  abstract =     "It is possible to argue that online bin packing
                 heuristics should be evaluated by using metrics based
                 on their performance over the set of all bin packing
                 problems, such as the worst case or average case
                 performance. However, this method of assessing a
                 heuristic would only be relevant to a user who employs
                 the heuristic over a set of problems which is actually
                 representative of the set of all possible bin packing
                 problems. On the other hand, a real world user will
                 often only deal with packing problems that are
                 representative of a particular sub-set. Their piece
                 sizes will all belong to a particular distribution. The
                 contribution of this paper is to show that a Genetic
                 Programming system can automate the process of
                 heuristic generation and produce heuristics that are
                 human-competitive over a range of sets of problems, or
                 which excel on a particular sub-set. We also show that
                 the choice of training instances is vital in the area
                 of automatic heuristic generation, due to the trade-off
                 between the performance and generality of the
                 heuristics generated and their applicability to new
                 problems.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Edmund Burke Matthew R Hyde Graham Kendall John R Woodward

Citations