INSPIRING FUTURES

Hyper-heuristics: learning to combine simple heuristics in bin-packing problems.

Ross, Peter, Schulenburg, Sonia, Marin-Blazquez, Javier G and Hart, Emma (2002) Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. In: Genetic and Evolutionary Computation Conference (GECCO), 9th-13th July 2002, New York.

Full text not available from this repository. (Request a copy)

Abstract/Description

Evolutionary algorithms (EAs) often appear to be a ‘black box’, neither offering worst-case bounds nor any guarantee of optimality when used to solve individual problems. They can also take much longer than non-evolutionary methods. We
try to address these concerns by using an EA, in
particular the learning classifier system XCS, to
learn a solution process rather than to solve individual
problems. The process chooses one of various simple non-evolutionary heuristics to apply to each state of a problem, gradually transforming the problem from its initial state to a solved state. We test this on a large set of one dimensional bin packing problems. For some of
the problems, none of the heuristics used can find
an optimal answer; however, the evolved solution
process can find an optimal solution in over 78% of cases.

Item Type: Conference or Workshop Item (Paper)
ISBN: 1558608788
Additional Information: Paper in: Langdon, W, B. ... et al. (eds.). GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. A Joint Meeting of the Seventh Annual Genetic Programming Conference (GP-2002) and the Eleventh International Conference on Genetic Algorithms (ICGA-2002). San Francisco, California: Morgan Kaufman, Nov. 2002. ISBNS 1558608788 and 9781558608788.
Uncontrolled Keywords: Evolutionary algorithms; Limitations; Learning solutions; Empirical process; Systematic packing;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods
500 Science > 510 Mathematics > 514 Topology
000 Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Library of Congress Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Item ID: 1843
Depositing User: RAE Import
Date Deposited: 22 Jul 2008 09:25
Last Modified: 11 Aug 2011 15:25
URI: http://researchrepository.napier.ac.uk/id/eprint/1843

Actions (login required)

View Item

Edinburgh Napier University is a registered Scottish charity. Registration number SC018373