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Mathematical Problems in Engineering
Volume 2013, Article ID 580876, 8 pages
Research Article

Genetic Pattern Search and Its Application to Brain Image Classification

1School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
2School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
3Brain Imaging Laboratory & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA

Received 1 July 2013; Revised 20 August 2013; Accepted 7 September 2013

Academic Editor: Vishal Bhatnagar

Copyright © 2013 Yudong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel global optimization method, based on the combination of genetic algorithm (GA) and generalized pattern search (PS) algorithm, is proposed to find global minimal points more effectively and rapidly. The idea lies in the facts that GA tends to be quite good at finding generally good global solutions, but quite inefficient in finding the last few mutations for the absolute optimum, and that PS is quite efficient in finding absolute optimum in a limited region. The novel algorithm, named as genetic pattern search (GPS), employs the GA as the search method at every step of PS. Experiments on five different classical benchmark functions (consisting of Hump, Powell, Rosenbrock, Schaffer, and Woods) demonstrate that the proposed GPS is superior to improved GA and improved PS with respect to success rate. We applied the GPS to the classification of normal and abnormal structural brain MRI images. The results indicate that GPS exceeds BP, MBP, IGA, and IPS in terms of classification accuracy. This suggests that GPS is an effective and viable global optimization method and can be applied to brain MRI classification.