Title | ||
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Designing and Comparing Multiple Portfolios of Parameter Configurations for Online Algorithm Selection. |
Abstract | ||
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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. |
Year | DOI | Venue |
---|---|---|
2016 | 10.1007/978-3-319-50349-3_7 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Online algorithm,Mathematical optimization,Hybrid algorithm,Computer science,Quadratic assignment problem,Portfolio,Travelling salesman problem,Artificial intelligence,Algorithm Selection,Machine learning,Iterated local search,Design of experiments | Conference | 10079 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.35 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Aldy Gunawan | 1 | 142 | 12.18 |
Hoong Chuin Lau | 2 | 739 | 91.69 |
Mustafa Misir | 3 | 5 | 1.76 |