Automatically searching for optimal parameter settings using a genetic algorithm

David S. Bolme; J. Ross Beveridge; Bruce A. Draper; P. Jonathon Phillips; Yui Man Lui

doi:10.1007/978-3-642-23968-7_22

Automatically searching for optimal parameter settings using a genetic algorithm

David S. Bolme, J. Ross Beveridge, Bruce A. Draper, P. Jonathon Phillips, Yui Man Lui

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings
Pages	213-222
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-23968-7_22
State	Published - 2011
Externally published	Yes
Event	8th International Conference on Computer Vision Systems, ICVS 2011 - Sophia Antipolis, France Duration: Sep 20 2011 → Sep 22 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6962 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Computer Vision Systems, ICVS 2011
Country/Territory	France
City	Sophia Antipolis
Period	09/20/11 → 09/22/11

Funding

This work was funded in part by the Technical Support Working Group (TSWG) under Task SC-AS-3181C. Jonathon Phillips was supported by the Department of Homeland Security, Director of National Intelligence, Federal Bureau of Investigation and National Institute of Justice. The identification of any commercial product or trade name does not imply endorsement or recommendation by Colorado State University or the National Institute of Standards and Technology.

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10.1007/978-3-642-23968-7_22

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Bolme, D. S., Beveridge, J. R., Draper, B. A., Phillips, P. J., & Lui, Y. M. (2011). Automatically searching for optimal parameter settings using a genetic algorithm. In Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings (pp. 213-222). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6962 LNCS). https://doi.org/10.1007/978-3-642-23968-7_22

Bolme, David S. ; Beveridge, J. Ross ; Draper, Bruce A. et al. / Automatically searching for optimal parameter settings using a genetic algorithm. Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings. 2011. pp. 213-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

author = "Bolme, {David S.} and Beveridge, {J. Ross} and Draper, {Bruce A.} and Phillips, {P. Jonathon} and Lui, {Yui Man}",

year = "2011",

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Bolme, DS, Beveridge, JR, Draper, BA, Phillips, PJ & Lui, YM 2011, Automatically searching for optimal parameter settings using a genetic algorithm. in Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6962 LNCS, pp. 213-222, 8th International Conference on Computer Vision Systems, ICVS 2011, Sophia Antipolis, France, 09/20/11. https://doi.org/10.1007/978-3-642-23968-7_22

Automatically searching for optimal parameter settings using a genetic algorithm. / Bolme, David S.; Beveridge, J. Ross; Draper, Bruce A. et al.
Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings. 2011. p. 213-222 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6962 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Bolme DS, Beveridge JR, Draper BA, Phillips PJ, Lui YM. Automatically searching for optimal parameter settings using a genetic algorithm. In Computer Vision Systems - 8th International Conference, ICVS 2011, Proceedings. 2011. p. 213-222. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-23968-7_22

Automatically searching for optimal parameter settings using a genetic algorithm

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