Abstract
Given the current glut of heuristic algorithms for the optimization of continuous functions, in some case characterized by complex schemes with parameters to be hand-tuned, it is an interesting research issue to assess whether competitive performance can be obtained by relying less on expert developers (whose intelligence can be a critical component of the success) and more on automated self-tuning schemes.
After a preliminary investigation about the applicability of record statistics, this paper proposes a fast reactive algorithm portfolio based on simple performance indicators: record value and iterations elapsed from the last record. The two indicators are used for a combined ranking and a stochastic replacement of the worst-performing members with a new searcher with random parameters or a perturbed version of a well-performing member.
The results on benchmark functions demonstrate a performance equivalent or better than that obtained by offline tuning schemes, which require a greater amount of CPU time and cannot take care of individual structural variations between different problem instances.
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Acknowledgments
The research of Roberto Battiti was supported by the Russian Science Foundation, project no. 15–11–30022 “Global optimization, supercomputing computations, and applications.”
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Brunato, M., Battiti, R. (2016). Extreme Reactive Portfolio (XRP): Tuning an Algorithm Population for Global Optimization. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_5
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