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A biased random-key genetic algorithm for the Steiner triple covering problem

Title
A biased random-key genetic algorithm for the Steiner triple covering problem
Type
Article in International Scientific Journal
Year
2012
Authors
Mauricio G C Resende
(Author)
Other
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Rodrigo F Toso
(Author)
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Jose Fernando Goncalves
(Author)
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Ricardo M A Silva
(Author)
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Journal
Title: Optimization LettersImported from Authenticus Search for Journal Publications
Vol. 6 No. 4
Pages: 605-619
ISSN: 1862-4472
Publisher: Springer Nature
Indexing
Scientific classification
FOS: Engineering and technology > Industrial biotechnology
CORDIS: Social sciences > Economics > Management studies
Other information
Authenticus ID: P-002-B8V
Abstract (EN): We present a biased random-key genetic algorithm (BRKGA) for finding small covers of computationally difficult set covering problems that arise in computing the 1-width of incidence matrices of Steiner triple systems. Using a parallel implementation of the BRKGA, we compute improved covers for the two largest instances in a standard set of test problems used to evaluate solution procedures for this problem. The new covers for instances A(405) and A(729) have sizes 335 and 617, respectively. On all other smaller instances our algorithm consistently produces covers of optimal size.
Language: English
Type (Professor's evaluation): Scientific
Contact: mgcr@research.att.com; rtoso@cs.rutgers.edu; jfgoncal@fep.up.pt; rmas@cin.ufpe.br
No. of pages: 15
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