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.
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Yuan, Z., de Oca, M.A.M., Birattari, M., Stützle, T. (2010). Modern Continuous Optimization Algorithms for Tuning Real and Integer Algorithm Parameters. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_18
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DOI: https://doi.org/10.1007/978-3-642-15461-4_18
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