This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parametertuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster in...
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This paper is concerned with automated classification of Combinatorial Optimization Problem instances for instance-specific parametertuning purpose. We propose the CluPaTra Framework, a generic approach to CLUster instances based on similar PAtterns according to search TRAjectories and apply it on parametertuning. 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.
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