Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Automatically Searching for Optimal Parameter Settings Using a Genetic Algorithm
verfasst von : David S. Bolme, J. Ross Beveridge, Bruce A. Draper, P. Jonathon Phillips, Yui Man Lui
Erschienen in: Computer Vision Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Modern vision systems are often a heterogeneous collection of image processing, machine learning, and pattern recognition techniques. One problem with these systems is finding their optimal parameter settings, since these systems often have many interacting parameters. This paper proposes the use of a Genetic Algorithm (GA) to automatically search parameter space. The technique is tested on a publicly available face recognition algorithm and dataset. In the work presented, the GA takes the role of a person configuring the algorithm by repeatedly observing performance on a tuning-subset of the final evaluation test data. In this context, the GA is shown to do a better job of configuring the algorithm than was achieved by the authors who originally constructed and released the LRPCA baseline. In addition, the data generated during the search is used to construct statistical models of the fitness landscape which provides insight into the significance from, and relations among, algorithm parameters.