- 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://gpbib.cs.ucl.ac.uk/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",
