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.
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This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative.
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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: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_7
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