The performance of an evolutionary algorithm strongly depends on the choice of the parameters which regulate its behavior. In this paper, two evolutionary algorithms (Particle Swarm Optimization and Differential Evolution) are used to nd the optimal configuration of parameters for Dierential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its eectiveness. Then, the same method was used to tune the parameters of Dierential Evolution in solving a real-world problem: the automatic localization of the hippocampus in histological brain images. The results obtained consistently outperformed the ones achieved using manually-tuned parameters. Thanks to a GPU-based implementation, our tuner is up to 8 times faster than the corresponding sequential version.

GPU-based Automatic Configuration of Differential Evolution: a case study / Ugolotti, Roberto; MESEJO SANTIAGO, Pablo; SHADY GEORGE NASHED, Youssef; Cagnoni, Stefano. - STAMPA. - 8154:(2013), pp. 114-125. ((Intervento presentato al convegno 16th Portuguese Conference on Artificial Intelligence (EPIA ’13) tenutosi a Angra do Heroismo, Azores nel 10-12 settembre 2013 [10.1007/978-3-642-40669-0_11].

GPU-based Automatic Configuration of Differential Evolution: a case study

UGOLOTTI, Roberto;MESEJO SANTIAGO, Pablo;SHADY GEORGE NASHED, Youssef;CAGNONI, Stefano
2013-01-01

Abstract

The performance of an evolutionary algorithm strongly depends on the choice of the parameters which regulate its behavior. In this paper, two evolutionary algorithms (Particle Swarm Optimization and Differential Evolution) are used to nd the optimal configuration of parameters for Dierential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its eectiveness. Then, the same method was used to tune the parameters of Dierential Evolution in solving a real-world problem: the automatic localization of the hippocampus in histological brain images. The results obtained consistently outperformed the ones achieved using manually-tuned parameters. Thanks to a GPU-based implementation, our tuner is up to 8 times faster than the corresponding sequential version.
9783642406683
GPU-based Automatic Configuration of Differential Evolution: a case study / Ugolotti, Roberto; MESEJO SANTIAGO, Pablo; SHADY GEORGE NASHED, Youssef; Cagnoni, Stefano. - STAMPA. - 8154:(2013), pp. 114-125. ((Intervento presentato al convegno 16th Portuguese Conference on Artificial Intelligence (EPIA ’13) tenutosi a Angra do Heroismo, Azores nel 10-12 settembre 2013 [10.1007/978-3-642-40669-0_11].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2616652
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