This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parameter tuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parameter tuning. The key idea is to use the search trajectory as a generic feature for clustering problem instances. The advantage of using search trajectory is that it can be obtained from any local-search based algorithm with small additional computation time. We explore and compare two different search trajectory representations, two sequence alignment techniques (to calculate similarities) as well as two well-known clustering methods. We report experiment results on two classical problems: Travelling Salesman Problem and Quadratic Assignment Problem and industrial case study.
generic feature, search trajectory, instance-based automated parameter tuning, sequence alignment, local search algorithm
Artificial Intelligence and Robotics | Software Engineering
Intelligent Systems and Decision Analytics; Software Systems
Journal of the Operational Research Society
Lindawati, Linda; LAU, Hoong Chuin; and LO, David.
Clustering of Search Trajectory and its Application to Parameter Tuning. (2013). Journal of the Operational Research Society. 64, (12), 1742-1752. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1806
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