A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Koohestani:2011:SGAI,
-
author = "Behrooz Koohestani and Riccardo Poli",
-
title = "A Hyper-Heuristic Approach to Evolving Algorithms for
Bandwidth Reduction Based on Genetic Programming",
-
booktitle = "Proceedings of the 31st SGAI International Conference
on Innovative Techniques and Applications of Artificial
Intelligence, AI-2011",
-
year = "2011",
-
editor = "Max Bramer and Miltos Petridis and Lars Nolle",
-
pages = "93--106",
-
address = "Cambridge, England",
-
publisher_address = "London",
-
month = dec,
-
organisation = "BCS special interest group on Artificial
Intelligence",
-
publisher = "Springer",
-
note = "Research and Development in Intelligent Systems
XXVIII, Incorporating Applications and Innovations in
Intelligent Systems XIX",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-1-4471-2318-7",
-
URL = "http://cswww.essex.ac.uk/staff/poli/papers/KoohestaniPoli2011_SGAI_conference.pdf",
-
DOI = "doi:10.1007/978-1-4471-2318-7_7",
-
size = "14 pages",
-
abstract = "The bandwidth reduction problem is a well-known
NP-complete graph layout problem that consists of
labelling the vertices of a graph with integer labels
in such a way as to minimise the maximum absolute
difference between the labels of adjacent vertices. The
problem is isomorphic to the important problem of
reordering the rows and columns of a symmetric matrix
so that its non-zero entries are maximally close to the
main diagonal, a problem which presents itself in a
large number of domains in science and engineering. A
considerable number of methods have been developed to
reduce the bandwidth, among which graph-theoretic
approaches are typically faster and more effective. a
hyper-heuristic approach based on genetic programming
is presented for evolving graph-theoretic bandwidth
reduction algorithms. The algorithms generated from our
hyper-heuristic are extremely effective. We test the
best of such evolved algorithms on a large set of
standard benchmarks from the Harwell-Boeing sparse
matrix collection against two state-of-the-art
algorithms from the literature. Our algorithm
outperforms both algorithms by a significant margin,
clearly indicating the promise of the approach.",
-
affiliation = "School of Computer Science and Electronic Engineering,
University of Essex, Colchester, CO4 3SQ, UK",
- }
Genetic Programming entries for
Behrooz Koohestani
Riccardo Poli
Citations