Real-World Parameter Tuning using Factorial Design with Parameter Decomposition
In this paper, we explore the idea of improving the efficiency of factorial design for parameter setting 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, we propose a decomposition of factorial design where parameters are partitioned into disjoint categories. One approach is to classify them as important and less-important parameters based on user guidance. Another proposed approach is to apply a fractional factorial design to partition parameters based on their main effect values. We empirically evaluate our approach against an existing automated parameter tuning configurator, namely ParamILS, to tune a simulated annealing algorithm for a real world spares inventory optimization problem. We conclude that our proposed methodology leads to improvements in terms of the quality of the solutions compared against the default para-meter values.