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Boosting evolutionary algorithm configuration

Boosting evolutionary algorithm configuration Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-020-09726-y
Publisher site
See Article on Publisher Site

Abstract

Algorithm configuration has emerged as an essential technology for the improvement of high-performance solvers. We present new algorithmic ideas to improve state-of-the-art solver configurators automatically by tuning. Particularly, we introduce 1. a forward-simulation method to improve parallel performance, 2. an improvement to the configuration process itself, and 3. a new technique for instance-specific solver configuration. Extensive experimental results show that the new solver configurator compares very favorably with the state-of-the-art in automatic configuration for combinatorial solvers.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: May 4, 2021

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