Automatically searching for optimal parameter settings using a genetic algorithm

Authors:
David S. Bolme;J. Ross Beveridge;Bruce A. Draper;P. Jonathon Phillips;Yui Man Lui
Affiliations:
Colorado State University, Fort Collins, CO;Colorado State University, Fort Collins, CO;Colorado State University, Fort Collins, CO;NIST, Gaithersburg, MD;Colorado State University, Fort Collins, CO
Venue:
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Year:
2011

Citing 6
Cited 0

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best

Proceedings of the 3rd International Conference on Genetic Algorithms
Discriminant Analysis of Principal Components for Face Recognition

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
Evolutionary tuning of SVM parameter values in multiclass problems

Neurocomputing
Object Detection with Discriminatively Trained Part-Based Models

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.