American Journal of Mathematical and Management Sciences - Modern digital simulation methodology, III
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Design and Analysis of Experiments
Design and Analysis of Experiments
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Corridor Selection and Fine Tuning for the Corridor Method
Learning and Intelligent Optimization
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
Tuning the performance of the MMAS heuristic
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
An integrated white+black box approach for designing and tuning stochastic local search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A math-heuristic algorithm for the DNA sequencing problem
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Time-bounded sequential parameter optimization
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
An improved memetic algorithm for the antibandwidth problem
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter search space of the important parameters. A model based on the Response Surface methodology is then proposed to define the promising initial range for the important parameter values. We show how our approach can be embedded with existing automated parameter tuning configurators, namely ParamILS and RCS (Randomized Convex Search), to tune target algorithms and demonstrate that our proposed methodology leads to improvements in terms of the quality of the solutions.