Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawback was the computational time. The underlying combination strategy consists in separated populations of agents evolving by means of different processes which share some individuals, from time to time. Experiments were performed to test the benefits of this paradigm in terms of computational time and correctness of the solutions.

Cipolla M, Lo Bosco G, Millonzi F, Valenti, C.F. (2011). A Memetic Island Model for Discrete Tomography Reconstruction. In A.M. Fanelli, W. Pedrycz, A. Petrosino (a cura di), Fuzzy logic and Applications, 9th International Workshop, WILF 2011 Trani, Italy, August 2011. Proceedings (pp. 261-268). Heidelberg : Springer Verlag [10.1007/978-3-642-23713-3_33].

A Memetic Island Model for Discrete Tomography Reconstruction

CIPOLLA, Marco;LO BOSCO, Giosue';MILLONZI, Filippo;VALENTI, Cesare Fabio
2011-01-01

Abstract

Soft computing is a term indicating a coalition of methodologies, and its basic dogma is that, in general, better results can be obtained through the use of constituent methodologies in combination, rather than in a stand alone mode. Evolutionary computing belongs to this coalition, and thus memetic algorithms. Here, we present a combination of several instances of a recently proposed memetic algorithm for discrete tomography reconstruction, based on the island model parallel implementation. The combination is motivated by the fact that, even though the results of the recently proposed approach are finally better and more robust compared to other approaches, we advised that its major drawback was the computational time. The underlying combination strategy consists in separated populations of agents evolving by means of different processes which share some individuals, from time to time. Experiments were performed to test the benefits of this paradigm in terms of computational time and correctness of the solutions.
2011
978-3-642-23712-6
978-3-642-23713-3
Cipolla M, Lo Bosco G, Millonzi F, Valenti, C.F. (2011). A Memetic Island Model for Discrete Tomography Reconstruction. In A.M. Fanelli, W. Pedrycz, A. Petrosino (a cura di), Fuzzy logic and Applications, 9th International Workshop, WILF 2011 Trani, Italy, August 2011. Proceedings (pp. 261-268). Heidelberg : Springer Verlag [10.1007/978-3-642-23713-3_33].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/60525
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