Modern continuous optimization algorithms for tuning real and integer algorithm parameters

Authors:
Zhi Yuan;Marco A. Montes De Oca;Mauro Birattari;Thomas Stützle
Affiliations:
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium
Venue:
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Year:
2010

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Abstract

To obtain peak performance from optimization algorithms, it is required to set appropriately their parameters. Frequently, algorithm parameters can take values from the set of real numbers, or from a large integer set. To tune this kind of parameters, it is interesting to apply state-of-the-art continuous optimization algorithms instead of using a tedious, and error-prone, hands-on approach. In this paper, we study the performance of several continuous optimization algorithms for the algorithm parameter tuning task. As case studies, we use a number of optimization algorithms from the swarm intelligence literature.