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Designing and comparing multiple portfolios of parameter configurations for online algorithm selection

Publication ,  Conference
Gunawan, A; Lau, HC; Mısır, M
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2016

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-ofbreed parameter tuning approaches directly for the comparison purpose. Experimental results show that our approach lead to improvements over best-of-breed parameter tuning approaches.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2016

Volume

10079 LNCS

Start / End Page

91 / 106

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Gunawan, A., Lau, H. C., & Mısır, M. (2016). Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10079 LNCS, pp. 91–106). https://doi.org/10.1007/978-3-319-50349-3_7
Gunawan, A., H. C. Lau, and M. Mısır. “Designing and comparing multiple portfolios of parameter configurations for online algorithm selection.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10079 LNCS:91–106, 2016. https://doi.org/10.1007/978-3-319-50349-3_7.
Gunawan A, Lau HC, Mısır M. Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2016. p. 91–106.
Gunawan, A., et al. “Designing and comparing multiple portfolios of parameter configurations for online algorithm selection.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10079 LNCS, 2016, pp. 91–106. Scopus, doi:10.1007/978-3-319-50349-3_7.
Gunawan A, Lau HC, Mısır M. Designing and comparing multiple portfolios of parameter configurations for online algorithm selection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2016. p. 91–106.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2016

Volume

10079 LNCS

Start / End Page

91 / 106

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences