Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 608-613.
Abstract
Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the strengths of multiple algorithmic approaches by training a classifier that selects or schedules solvers dependent on the given instance. We devise a new classifier that selects solvers based on a cost-sensitive hierarchical clustering model. Experimental results on SAT and MaxSAT show that the new method outperforms the most effective portfolio builders to date.
@inproceedings{ijcai2013p97,
title = {Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering},
author = {Malitsky, Yuri and Sabharwal, Ashish and Samulowitz, Horst and Sellmann, Meinolf},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {608--613},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/97},
}
TY - CPAPER TI - Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering AU - Malitsky, Yuri AU - Sabharwal, Ashish AU - Samulowitz, Horst AU - Sellmann, Meinolf T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 608 EP - 613 UR - https://ijcai.org/proceedings/2013/97 ER -Copied
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