The University of Southampton
University of Southampton Institutional Repository

Anatomy of the fitness landscape for dense graph-colouring problem

Anatomy of the fitness landscape for dense graph-colouring problem
Anatomy of the fitness landscape for dense graph-colouring problem
Graph-colouring is one of the best-known combinatorial optimisation problems. This paper provides a systematic analysis of many properties of the fitness landscape for random instances as a function of both the problem size and the number of colours used. The properties studied include both statistical properties of the bulk of the states, such as the distribution of fitnesses and the auto-correlation, but also properties related to the local optima of the problem. These properties include the mean time to reach the local optima, the number of local optima and the probability of reaching local optima of a given cost, the average distance between global optima and between local optima of a given cost and the closest local optimum, the expected cost as a function of the distance from a configuration and the fitness–distance correlation. Finally, an analysis of how a successful algorithm exploits the fitness distance correlation is carried out.
47-65
Tayarani-N, M. H.
da003cbc-3d35-4aaa-aa8d-9437b720bfec
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Tayarani-N, M. H.
da003cbc-3d35-4aaa-aa8d-9437b720bfec
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Tayarani-N, M. H. and Prugel-Bennett, Adam (2015) Anatomy of the fitness landscape for dense graph-colouring problem. Journal of Swarm and Evolutionary Computation, 22, 47-65. (doi:10.1016/j.swevo.2015.01.005).

Record type: Article

Abstract

Graph-colouring is one of the best-known combinatorial optimisation problems. This paper provides a systematic analysis of many properties of the fitness landscape for random instances as a function of both the problem size and the number of colours used. The properties studied include both statistical properties of the bulk of the states, such as the distribution of fitnesses and the auto-correlation, but also properties related to the local optima of the problem. These properties include the mean time to reach the local optima, the number of local optima and the probability of reaching local optima of a given cost, the average distance between global optima and between local optima of a given cost and the closest local optimum, the expected cost as a function of the distance from a configuration and the fitness–distance correlation. Finally, an analysis of how a successful algorithm exploits the fitness distance correlation is carried out.

Text
Anatomy of the Fitness Landscape for Dense Graph-Colouring Problem - Proof
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 14 January 2015
e-pub ahead of print date: 9 March 2015
Published date: June 2015
Additional Information: This is AM on publisher template.

Identifiers

Local EPrints ID: 412227
URI: http://eprints.soton.ac.uk/id/eprint/412227
PURE UUID: 4a86fd54-1130-4b12-868d-cf8551e0ac23

Catalogue record

Date deposited: 14 Jul 2017 16:30
Last modified: 03 Mar 2020 17:41

Export record

Altmetrics

Contributors

Author: M. H. Tayarani-N
Author: Adam Prugel-Bennett

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×