Publication Type
Conference Proceeding Article
Version
Postprint
Publication Date
6-2016
Abstract
Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We apply our approach on a Simulated Annealing-Tabu Search (SA-TS) hybrid algorithm for solving the Quadratic Assignment Problem (QAP) as well as an Iterated Local Search (ILS) on the Travelling Salesman Problem (TSP). We also generate a portfolio of parameter configurations using best-of-breed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms
Research Areas
Intelligent Systems and Decision Analytics
Publication
Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers
Volume
10079
First Page
91
Last Page
106
ISBN
9783319503493
Identifier
10.1007/978-3-319-50349-3_7
Publisher
Springer
City or Country
Cham
Citation
GUNAWAN, Aldy; LAU, Hoong Chuin; and MISIR, Mustafa.
Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. (2016). Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers. 10079, 91-106. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3308
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Additional URL
http://doi.org/10.1007/978-3-319-50349-3_7
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Theory and Algorithms Commons