Abstract
Configuring algorithms automatically to achieve high performance is becoming increasingly relevant and important in many areas of academia and industry. Algorithm configuration methods take a parameterized target algorithm, a performance metric and a set of example data, and aim to find a parameter configuration that performs as well as possible on a given data set.
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References
Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009)
Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated F-race: an overview. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Empirical Methods for the Analysis of Optimization Algorithms. Springer, Heidelberg (2010)
Hutter, F., Babić, D., Hoos, H.H., Hu, A.J.: Boosting verification by automatic tuning of decision procedures. In: Formal Methods in Computer Aided Design, pp. 27–34 (2007)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 5. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Int. Res. 36(1), 267–306 (2009)
Kotthoff, L.: Reliability of computational experiments on virtualised hardware. JETAI (2013)
Lampe, U., Kieselmann, M., Miede, A., Zöller, S., Steinmetz, R.: A tale of millis and nanos: time measurements in virtual and physical machines. In: Lau, K.-K., Lamersdorf, W., Pimentel, E. (eds.) ESOCC 2013. LNCS, vol. 8135, pp. 172–179. Springer, Heidelberg (2013)
Schad, J., Dittrich, J., Quiané-Ruiz, J.-A.: Runtime measurements in the cloud: observing, analyzing, and reducing variance. VLDB Endow. 3, 460–471 (2010)
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: KDD, pp. 847–855 (2013)
Acknowledgements
The authors were supported by an Amazon Web Services research grant, European Union FP7 grant 284715 (ICON), a DFG Emmy Noether Grant, and Compute Canada.
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Geschwender, D., Hutter, F., Kotthoff, L., Malitsky, Y., Hoos, H.H., Leyton-Brown, K. (2014). Algorithm Configuration in the Cloud: A Feasibility Study. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_5
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DOI: https://doi.org/10.1007/978-3-319-09584-4_5
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