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.

Extreme reactive portfolio (XRP): Tuning an algorithm population for global optimization / Brunato, Mauro; Battiti, Roberto. - STAMPA. - 10079:(2016), pp. 60-74. (Intervento presentato al convegno LION 10 tenutosi a Ischia, IT nel 29th May -1st June 2016) [10.1007/978-3-319-50349-3_5].

Extreme reactive portfolio (XRP): Tuning an algorithm population for global optimization

Brunato, Mauro;Battiti, Roberto
2016-01-01

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.
2016
Learning and Intelligent Optimization: 10th International Conference, LION 10, Revised Selected Papers
Cham, CH
Springer
9783319503486
978-3-319-50349-3
Brunato, Mauro; Battiti, Roberto
Extreme reactive portfolio (XRP): Tuning an algorithm population for global optimization / Brunato, Mauro; Battiti, Roberto. - STAMPA. - 10079:(2016), pp. 60-74. (Intervento presentato al convegno LION 10 tenutosi a Ischia, IT nel 29th May -1st June 2016) [10.1007/978-3-319-50349-3_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/166660
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