Real-world parameter tuning using factorial design with parameter decomposition

• Main Authors: GUNAWAN, Aldy, LAU, Hoong Chuin, WONG, Elaine
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/1612; https://ink.library.smu.edu.sg/context/sis_research/article/2611/viewcontent/RealWorldParameterTuning_2013_AdvMetaheuristics.pdf
• Institution: Singapore Management University

In this paper, we explore the idea of improving the efficiency of factorial design for parameter tuning of metaheuristics. In a standard full factorial design, the number of runs increases exponentially as the number of parameters. To reduce the parameter search space, one option is to first partition parameters into disjoint categories. While this may be done manually based on user guidance, an automated approach proposed in this paper is to apply a fractional factorial design to partition parameters based on their main effects where each partition is then tuned independently. With a careful choice of fractional design, our approach yields a linear rather than exponential run time performance with respect to the number of parameters. We empirically evaluate our approach for tuning a simulated annealing algorithm that solves an industry spares inventory optimization problem. We show that our proposed methodology leads to improvements in terms of the quality of solutions when compared to a pure application of an automated parameter tuning configurator ParamILS.