The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms / Iacca, Giovanni; Neri, Ferrante; Caraffini, Fabio; Suganthan, Ponnuthurai Nagaratnam. - (2014), pp. 615-626. (Intervento presentato al convegno EvoApplications 2014 tenutosi a Granada nel 23rd April-25th April 2014) [10.1007/978-3-662-45523-4_50].

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

Iacca, Giovanni;
2014-01-01

Abstract

The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms.
2014
Applications of Evolutionary Computation
Berlin, Heidelberg
Springer
978-3-662-45522-7
978-3-662-45523-4
Iacca, Giovanni; Neri, Ferrante; Caraffini, Fabio; Suganthan, Ponnuthurai Nagaratnam
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms / Iacca, Giovanni; Neri, Ferrante; Caraffini, Fabio; Suganthan, Ponnuthurai Nagaratnam. - (2014), pp. 615-626. (Intervento presentato al convegno EvoApplications 2014 tenutosi a Granada nel 23rd April-25th April 2014) [10.1007/978-3-662-45523-4_50].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/196430
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