- 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.",
