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Effective collaborative strategies to setup tuners

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Abstract

Parameter setting problem has demonstrated being a relevant problem related to the use of metaheuristics. ParamILS and I-Race are sophisticated tuning methods that can provide valuable information for designers as well as manage conditional parameters. However, the quality of parameter configurations they can find strongly depends on a proper definition of parameter search space. Evoca is a recently proposed tuner which has demonstrated being less sensitive to the setup of parameters search space. In this paper, we propose an effective collaborative approach that combines Evoca and I-Race as well as Evoca and ParamILS. In both collaborative strategies, Evoca is used to define a proper parameter search space for each tuner. Results demonstrated that the collaborative approaches studied are able to find good parameter configurations reducing the effort required to properly define the parameter search space.

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Acknowledgements

This work was supported by the FONDECYT Initiation into Research Project 11150787, Centro Científico Tecnológico de Valparaíso (CCTVal) FB0821 and UTFSM Project PI_LI_19_16.

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Authors and Affiliations

  1. Universidad Andres Bello, Viña del Mar, Chile

    Elizabeth Montero

  2. Universidad Técnica Federico Santa María, Valparaíso, Chile

    Elizabeth Montero & María-Cristina Riff

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  1. Elizabeth Montero
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  2. María-Cristina Riff
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Correspondence to Elizabeth Montero or María-Cristina Riff.

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Communicated by V. Loia.

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Montero, E., Riff, MC. Effective collaborative strategies to setup tuners. Soft Comput 24, 5019–5041 (2020). https://doi.org/10.1007/s00500-019-04252-4

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