Integrated Genetic Algorithm with Hill Climbing for the Bandwidth Minimization Porblem
Publication Type
Conference Proceeding Article
Publication Date
7-2003
Abstract
In this paper, we propose an integrated Genetic Algorithm with Hill Climbing to solve the matrix bandwidth minimization problem, which is to reduce bandwidth by permuting rows and columns resulting in the nonzero elements residing in a band as close as possible to the diagonal. Experiments show that this approach achieves the best solution quality when compared with the GPS [1] algorithm, Tabu Search [3], and the GRASP with Path Relinking methods [4], while being faster than the latter two newly-developed heuristics.
Discipline
Operations and Supply Chain Management
Research Areas
Operations Management
Publication
Genetic and Evolutionary Computation - GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, July 12–16, 2003 Proceedings, Part II
Volume
2724
First Page
1594
Last Page
1595
ISBN
9783540451105
Identifier
10.1007/3-540-45110-2_41
Publisher
Springer
City or Country
Berlin
Citation
LIM, Andrew; RODRIGUES, Brian; and XIAO, Fei.
Integrated Genetic Algorithm with Hill Climbing for the Bandwidth Minimization Porblem. (2003). Genetic and Evolutionary Computation - GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, July 12–16, 2003 Proceedings, Part II. 2724, 1594-1595.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/2068
Additional URL
https://doi.org/10.1007/3-540-45110-2_41