Clustering of Search Trajectory and its Application to Parameter Tuning
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...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2013
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1806 https://ink.library.smu.edu.sg/context/sis_research/article/2805/viewcontent/CluPaTra_preprint.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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