GPU-based automatic configuration of differential evolution: A case study

Roberto Ugolotti, Pablo Mesejo, Youssef S.G. Nashed, Stefano Cagnoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProgress in Artificial Intelligence - 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Proceedings
Number of pages12
StatePublished - 2013
Event16th Portuguese Conference on Artificial Intelligence, EPIA 2013 - Angra do Heroismo, Azores, Portugal
Duration: Sep 9 2013Sep 12 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8154 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other16th Portuguese Conference on Artificial Intelligence, EPIA 2013
CityAngra do Heroismo, Azores


  • Automatic Algorithm Configuration
  • Differential Evolution
  • Global Continuous Optimization
  • Particle Swarm Optimization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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