QUT ePrints

A genetic algorithm for the multi-source and multi-sink minimum vertex cut problem and its applications

Tang, Maolin & Fidge, Colin J. (2009) A genetic algorithm for the multi-source and multi-sink minimum vertex cut problem and its applications. In IEEE Congress on Evolutionary Computation, 18-21 May 2009, Nova Conference Centre and Cinema, Trondheim.

View at publisher

Abstract

We present a new penalty-based genetic algorithm for the multi-source and multi-sink minimum vertex cut problem, and illustrate the algorithm’s usefulness with two real-world applications. It is proved in this paper that the genetic algorithm always produces a feasible solution by exploiting some domain-specific knowledge. The genetic algorithm has been implemented on the example applications and evaluated to show how well it scales as the problem size increases.

Impact and interest:

1 citations in Scopus
Search Google Scholar™
1 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

175 since deposited on 01 Oct 2009
28 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 27672
Item Type: Conference Paper
Keywords: genetic algorithm, minimal cut, optimization
DOI: 10.1109/CEC.2009.4983353
ISBN: 9781424429585
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2009 IEEE
Copyright Statement: © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 02 Oct 2009 07:37
Last Modified: 29 Feb 2012 23:57

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page