TY - GEN
T1 - GPU-based automatic configuration of differential evolution
T2 - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013
AU - Ugolotti, Roberto
AU - Mesejo, Pablo
AU - Nashed, Youssef S.G.
AU - Cagnoni, Stefano
PY - 2013
Y1 - 2013
N2 - 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 find the optimal configuration of parameters for Differential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its effectiveness. Then, the same method was used to tune the parameters of Differential 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.
AB - 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 find the optimal configuration of parameters for Differential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its effectiveness. Then, the same method was used to tune the parameters of Differential 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.
KW - Automatic Algorithm Configuration
KW - Differential Evolution
KW - GPGPU
KW - Global Continuous Optimization
KW - Particle Swarm Optimization
UR - http://www.scopus.com/inward/record.url?scp=84884714576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884714576&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40669-0_11
DO - 10.1007/978-3-642-40669-0_11
M3 - Conference contribution
AN - SCOPUS:84884714576
SN - 9783642406683
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 125
BT - Progress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings
Y2 - 9 September 2013 through 12 September 2013
ER -