Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 4523754, 13 pages
https://doi.org/10.1155/2017/4523754
Research Article

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

School of Computer, Shenyang Aerospace University, Shenyang 110136, China

Correspondence should be addressed to Shoufei Han; moc.liamg@iefuohsnah

Received 29 June 2017; Accepted 3 August 2017; Published 11 September 2017

Academic Editor: Luis Vergara

Copyright © 2017 Xiguang Li 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. D. Goldberg, Genetic Algorithms in Search, Addison-Wesley, Mass, USA, 1989.
  2. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 26, no. 1, pp. 29–41, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN ’95), vol. 4, pp. 1942–1948, Perth, WA, Australia, November-December 1995. View at Publisher · View at Google Scholar · View at Scopus
  4. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science (MHS '95), pp. 39–43, Nagoya, Japan, October 1995. View at Publisher · View at Google Scholar · View at Scopus
  5. R. A. Formato, “Central force optimization: a new metaheuristic with applications in applied electromagnetics,” Progress in Electromagnetics Research, vol. 77, pp. 425–491, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. W. Y. Qian and T. T. Zhang, “Adaptive central force optimization algorithm,” Computer Science, vol. 39, pp. 207–209, 2012. View at Google Scholar
  7. P. W. Shor, “Algorithms for quantum computation: discrete logarithms and factoring,” in Proceedings of the 35th Annual Symposium on Foundations of Computer Science (SFCS '94), pp. 124–134, IEEE, New York, USA, 1994. View at Publisher · View at Google Scholar
  8. L. M. Adleman, “Molecular computation of solutions to combinatorial problems,” Science, vol. 266, no. 5187, pp. 1021–1024, 1994. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Teodorovic and M. Dell'Orco, “Bee colony optimization-a cooperative learning approach to complex transportation problems. advanced OR and AI methods in transportation,” in Proceedings of the In Proceedings of the 10th EWGT Meeting and 16th Mini-EURO Conference, pp. 51–60, Poznan, Poland, 2005.
  10. H. Bersini and F. Varela, “The immune recruitment mechanism: a selective evolutionary strategy,” in Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 520–526, University of California, San Diego, Calif, USA, July 1991.
  11. X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO '10), J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., vol. 284 of Studies in Computational Intelligence, pp. 65–74, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  12. S. Zheng, A. Janecek, and Y. Tan, “Enhanced fireworks algorithm,” in Proceedings of the IEEE Congress on Evolutionary Computation, vol. 62, pp. 2069–2077, Cancun, Mexico, June 2013. View at Publisher · View at Google Scholar
  13. Q. He and L. Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design problems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 1, pp. 89–99, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. E. M. Montes and C. A. C. Coello, “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems,” International Journal of General Systems, vol. 37, no. 4, pp. 443–473, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  15. C. A. C. Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Computers in Industry, vol. 41, no. 2, pp. 113–127, 2000. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Applied Mathematics and Computation, vol. 188, no. 2, pp. 1567–1579, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  17. F. Huang, L. Wang, and Q. He, “An effective co-evolutionary differential evolution for constrained optimization,” Applied Mathematics and Computation, vol. 186, no. 1, pp. 340–356, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  18. A. Carlos and C. Coello, “Constraint-handling using an evolutionary multiobjective optimization technique,” Civil Engineering and Environmental Systems, vol. 17, pp. 319–346, 2000. View at Google Scholar
  19. K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2–4, pp. 311–338, 2000. View at Publisher · View at Google Scholar
  20. K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Computer Methods in Applied Mechanics and Engineering, vol. 194, no. 36–38, pp. 3902–3933, 2005. View at Publisher · View at Google Scholar
  21. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990, Budapest, Hungary, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. J. F. Chen, “Comparison of fusion methods for multi-neural network classifier,” Popular Technology, vol. 9, pp. 30–32, 2011. View at Google Scholar
  23. A. Soriano, L. Vergara, B. Ahmed, and A. Salazar, “Fusion of scores in a detection context based on Alpha integration,” Neural Computation, vol. 27, no. 9, pp. 1983–2010, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Vergara, A. Soriano, G. Safont, and A. Salazar, “On the fusion of non-independent detectors,” Digital Signal Processing: a Review Journal, vol. 50, pp. 24–33, 2016. View at Publisher · View at Google Scholar · View at Scopus