Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 580876, 8 pages
http://dx.doi.org/10.1155/2013/580876
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.

Linked References

  1. G. Corriveau, R. Guilbault, and A. Tahan, “Genetic algorithms and finite element coupling for mechanical optimization,” Advances in Engineering Software, vol. 41, no. 3, pp. 422–426, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  2. M. Wetter and E. Polak, “Building design optimization using a convergent pattern search algorithm with adaptive precision simulations,” Energy and Buildings, vol. 37, no. 6, pp. 603–612, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Zhang, L. Wu, and S. Wang, “UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization,” Mathematical Problems in Engineering, vol. 2013, Article ID 705238, 9 pages, 2013. View at Publisher · View at Google Scholar
  4. Y. Zhang, L. Wu, and S. Wang, “Solving two-dimensional HP model for firefly algorithm and simplified energy function,” Mathematical Problems in Engineering, vol. 2013, Article ID 398141, 9 pages, 2013. View at Google Scholar · View at MathSciNet
  5. L. Moreno, S. Garrido, D. Blanco, and M. L. Muñoz, “Differential evolution solution to the SLAM problem,” Robotics and Autonomous Systems, vol. 57, no. 4, pp. 441–450, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Verboomen, D. Van Hertem, P. H. Schavemaker et al., “Phase shifter coordination for optimal transmission capacity using particle swarm optimization,” Electric Power Systems Research, vol. 78, no. 9, pp. 1648–1653, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Kuroki, G. S. Young, and S. E. Haupt, “UAV navigation by an expert system for contaminant mapping with a genetic algorithm,” Expert Systems with Applications, vol. 37, no. 6, pp. 4687–4697, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Kaya, “A genetic algorithm approach to determine the sample size for attribute control charts,” Information Sciences, vol. 179, no. 10, pp. 1552–1566, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Kumar and C. S. P. Rao, “Application of ant colony, genetic algorithm and data mining-based techniques for scheduling,” Robotics and Computer-Integrated Manufacturing, vol. 25, no. 6, pp. 901–908, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Z. Jahromi and M. Taheri, “A proposed method for learning rule weights in fuzzy rule-based classification systems,” Fuzzy Sets and Systems, vol. 159, no. 4, pp. 449–459, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  11. A. Jamali, A. Hajiloo, and N. Nariman-zadeh, “Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA),” Expert Systems with Applications, vol. 37, no. 1, pp. 401–413, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Suga, S. Kato, and K. Hiyama, “Structural analysis of Pareto-optimal solution sets for multi-objective optimization: An application to outer window design problems using Multiple Objective Genetic Algorithms,” Building and Environment, vol. 45, no. 5, pp. 1144–1152, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. N. Orlic and S. Loncaric, “Earthquake-explosion discrimination using genetic algorithm-based boosting approach,” Computers and Geosciences, vol. 36, no. 2, pp. 179–185, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. C.-W. Tsai, C.-H. Huang, and C.-L. Lin, “Structure-specified IIR filter and control design using real structured genetic algorithm,” Applied Soft Computing Journal, vol. 9, no. 4, pp. 1285–1295, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Araujo, H. Zaragoza, J. R. Pérez-Agüera, and J. Pérez-Iglesias, “Structure of morphologically expanded queries: a genetic algorithm approach,” Data and Knowledge Engineering, vol. 69, no. 3, pp. 279–289, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Bogani, M. G. Gasparo, and A. Papini, “Generalized pattern search methods for a class of nonsmooth optimization problems with structure,” Journal of Computational and Applied Mathematics, vol. 229, no. 1, pp. 283–293, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  17. X. Song, H. Gu, X. Zhang, and J. Liu, “Pattern search algorithms for nonlinear inversion of high-frequency Rayleigh-wave dispersion curves,” Computers and Geosciences, vol. 34, no. 6, pp. 611–624, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Zhang, L. Wu, Y. Huoc, and S. Wang, “A novel global optimization method- Genetic pattern search,” Applied Mechanics and Materials, vol. 44-47, pp. 3240–3244, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. L. De Giovanni and F. Pezzella, “An improved genetic algorithm for the distributed and flexible job-shop scheduling problem,” European Journal of Operational Research, vol. 200, no. 2, pp. 395–408, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. T. A. Sriver, J. W. Chrissis, and M. A. Abramson, “Pattern search ranking and selection algorithms for mixed variable simulation-based optimization,” European Journal of Operational Research, vol. 198, no. 3, pp. 878–890, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. L. D. S. Coelho, J. G. Sauer, and M. Rudek, “Differential evolution optimization combined with chaotic sequences for image contrast enhancement,” Chaos, Solitons and Fractals, vol. 42, no. 1, pp. 522–529, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Ghanbari and N. Mahdavi-Amiri, “Solving bus terminal location problems using evolutionary algorithms,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 991–999, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Chaplot, L. M. Patnaik, and N. R. Jagannathan, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network,” Biomedical Signal Processing and Control, vol. 1, no. 1, pp. 86–92, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. E.-S. A. El-Dahshan, T. Hosny, and A.-B. M. Salem, “Hybrid intelligent techniques for MRI brain images classification,” Digital Signal Processing: A Review Journal, vol. 20, no. 2, pp. 433–441, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Zhang, Z. Dong, L. Wu, and S. Wang, “A hybrid method for MRI brain image classification,” Expert Systems with Applications, vol. 38, no. 8, pp. 10049–10053, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. X. Delaunay, M. Chabert, V. Charvillat, and G. Morin, “Satellite image compression by post-transforms in the wavelet domain,” Signal Processing, vol. 90, no. 2, pp. 599–610, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. J. Camacho, J. Picó, and A. Ferrer, “Data understanding with PCA: structural and variance information plots,” Chemometrics and Intelligent Laboratory Systems, vol. 100, no. 1, pp. 48–56, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. R. J. May, H. R. Maier, and G. C. Dandy, “Data splitting for artificial neural networks using SOM-based stratified sampling,” Neural Networks, vol. 23, no. 2, pp. 283–294, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Kordík, J. Koutník, J. Drchal, O. Kovářík, M. Čepek, and M. Šnorek, “Meta-learning approach to neural network optimization,” Neural Networks, vol. 23, no. 4, pp. 568–582, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Barmpalexis, F. I. Kanaze, K. Kachrimanis, and E. Georgarakis, “Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation,” European Journal of Pharmaceutics and Biopharmaceutics, vol. 74, no. 2, pp. 316–323, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. Z. W. Geem and W. E. Roper, “Energy demand estimation of South Korea using artificial neural network,” Energy Policy, vol. 37, no. 10, pp. 4049–4054, 2009. View at Publisher · View at Google Scholar · View at Scopus