Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

8-2014

Abstract

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration.

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Publication

Learning and Intelligent Optimization: 8th International Conference, Lion 8, Gainesville, FL, USA, February 16-21, 2014. Revised Selected Papers

Volume

8426

First Page

62

Last Page

76

ISBN

9783319095844

Identifier

10.1007/978-3-319-09584-4_7

Publisher

Springer Verlag

City or Country

Cham

Additional URL

http://dx.doi.org/10.1007/978-3-319-09584-4_7

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