Using smart sampling to discover promising regions and increase the efficiency of differential evolution

ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications

Volumn , Issue , 2009, Pages 1394-1399

  Melo, Vinicius V ^a   Delbem, Alexandre C B ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK FUNCTIONS; DIFFERENTIAL EVOLUTION; GLOBAL OPTIMUM; MACHINE LEARNING TECHNIQUES; METAHEURISTIC; NOVEL METHODS; OPTIMIZATION PROBLEMS; SEARCH SPACES;

EVOLUTIONARY ALGORITHMS; GLOBAL OPTIMIZATION; HEURISTIC METHODS; INTELLIGENT SYSTEMS; MATHEMATICAL OPERATORS; THREE TERM CONTROL SYSTEMS;

LEARNING ALGORITHMS;

EID: 77949536131     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2009.248     Document Type: Conference Paper

References (18)

1. D. W. Aha, D. Kibler, and M. K. Albert. Instancebased learning algorithms. In Machine Learning, pages 37-66, 1991.
2. P. Alevizos and D. Tasoulis. Improving the orthogonal range search k-windows algorithm. In In Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence, pages 239-245, 2002.
3. D. Ardia. Deoptim package: Differential evolution optimization in r language, 2008.
4. U. K. Chakraborty. Advances in Differential Evolution. Springer Publishing Company, Incorporated, 2008.
5. R. Chelouah and P. Siarry. Genetic and nelder-mead algorithms hybridized for a more accurate global optimization of continuous multi-minima functions. European Journal of Operational Research., 148(2):335-348, 2003.
6. W. W. Cohen. Fast effective rule induction. In In Proceedings of the Twelfth International Conference on Machine Learning, pages 115-123. Morgan Kaufmann, 1995.
7. K. Deb and D. Goldberg. An investigation of niche and species formation in genetic function optimization. In In Schaffer, J., editor, Proceedings of the Third International Conference on Genetic Algorithms, pages 42-50, San Mateo, CA, USA, 1989. Morgan Kaufmann.
8. M. Jelasity, P. Ortigosa, and I. Garcia. Uego, an abstract clustering technique for multimodal global optimization. Journal of Heuristics., 7(3):215-233, 2001.
9. V. V. Melo, A. C. B. Delbem, D. L. Pinto Junior, and F. M. Federson. Discovering promising regions to help global numerical optimization algorithms. In MICAI, pages 72-82, 2007.
10. T. M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
11. A. C. M. Oliveira and L. A. N. Lorena. Hybrid evolutionary algorithms and clustering search. In In: Crina Grosan,Ajith Abraham and Hisao Ishibuchi (eds) Hybrid Evolutionary Systems - Studies in Computational Intelligence - Springer SCI Series. 75, pages 81-102, 2007.
12. P. M. Ortigosa, I. Garcia, I. García, and M. Jelasity. Reliability and performance of uego, a clusteringbased global optimizer. Journal of Global Optimization, 19:2001, 2001.
13. S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama. Opposition versus randomness in soft computing techniques. Appl. Soft Comput., 8(2):906-918, 2008.
14. R. Storn and K. Price. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341-359, December 1997.
15. P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari. Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. Technical Report KanGAL Report 2005005, Nanyang Technological University, Singapore, 2005.
16. D. K. Tasoulis, V. P. Plagianakos, and M. N. Vrahatis. Clustering in evolutionary algorithms to effi-ciently compute simultaneously local and global minima. In Congress on Evolutionary Computation, pages 1847-1854, 2005.
17. H. R. Tizhoosh. Opposition-based learning: A new scheme for machine intelligence.
18. M. N. Vrahatis, B. Boutsinas, P. Alevizos, and G. Pavlides. The new k-windows algorithm for improving the k-means clustering algorithm. J. Complex., 18(1):375-391, 2002.