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Algorithm configuration: Learning policies for the quick termination of poor performers


Daniel Karapetyan



© 2019, Springer Nature Switzerland AG. One way to speed up the algorithm configuration task is to use short runs instead of long runs as much as possible, but without discarding the configurations that eventually do well on the long runs. We consider the problem of selecting the top performing configurations of Conditional Markov Chain Search (CMCS), a general algorithm schema that includes, for example, VNS. We investigate how the structure of performance on short tests links with those on long tests, showing that significant differences arise between test domains. We propose a “performance envelope” method to exploit the links; that learns when runs should be terminated, but that automatically adapts to the domain.


Karapetyan, D., Parkes, A. J., & Stützle, T. (2019). Algorithm configuration: Learning policies for the quick termination of poor performers. .

Conference Name LION 12 Learning and Intelligent Optimization Conference
Start Date Jun 10, 2018
End Date Jun 15, 2018
Acceptance Date May 14, 2018
Online Publication Date Dec 31, 2018
Publication Date Jan 1, 2019
Deposit Date Mar 17, 2019
Publicly Available Date Mar 18, 2019
Journal Lecture Notes in Computer Science; Learning and Intelligent Optimization
Electronic ISSN 1611-3349
Publisher Springer Verlag
Volume 11353 LNCS
Pages 220-224
Series Title Lecture notes in computer science
Series Number 11353
Series ISSN 0302-9743
ISBN 9783030053475
Public URL
Publisher URL
Additional Information Conference Acronym: LION 12; Conference Name: International Conference on Learning and Intelligent Optimization; Conference City: Kalamata; Conference Country: Greece; Conference Year: 2018; Conference Start Date: 10 June 2018; Conference End Date: 15 June 2018; Conference Number: 12; Conference ID: lion2018; Conference URL:


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