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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yuan, Z., De Oca, M. A. M., Birattari, M., & Stützle, T. (2010). Modern continuous optimization algorithms for tuning real and integer algorithm parameters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6234 LNCS, pp. 203–214). https://doi.org/10.1007/978-3-642-15461-4_18
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