Parallel enhanced hybrid evolutionary algorithm for continuous function optimization

Proceedings - 2012 7th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2012

Volumn , Issue , 2012, Pages 125-131

  Said, Said Mohamed ^a   Nakamura, Morikazu ^a  

^a UNIVERSITY OF THE RYUKYUS   (Japan)

Author keywords

Continuous functions; Estimation of Distribution Algorithm; Genetic Algorithms; Hybrid; K means clustering; Master Slave; Parallel processing

Indexed keywords

CONTINUOUS FUNCTIONS; ESTIMATION OF DISTRIBUTION ALGORITHMS; HYBRID; K-MEANS CLUSTERING; MASTER-SLAVE; PARALLEL PROCESSING;

COMPUTER ARCHITECTURE; FUNCTIONS; GENETIC ALGORITHMS; INTERNET;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 84871559666     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/3PGCIC.2012.51     Document Type: Conference Paper

References (15)

1. L. Changhe, and Y. Shengxiang, An Island Based Hybrid Evolutionary Algorithm for Optimization, Simulated Evolution and Learning, LNCS, vol. 5361, pp. 180-189, 2008.
2. H. Mühlenbein, and G. Paa From recombination in genes to the estimation of distribution 1, binary parameters, Lecture Notes in Computer Science 1411, Parallel Problem Solving from Nature, 1996.
3. T. Niknam, B. Amiri, J. Olamaei, and A. Arefi, An Efficient Hybrid Evolutionary Optimization Algorithm based on PSO and SA for clustering, JZU SC. A , vol 10, No 4, 512-519, 2008.
4. T. Tometzki, and S. Engell, Hybrid evolutionary optimization of two-stage stochastic integer programming problems, An empirical investigation, Evol Comput, Winter 17(4) 511-526, 2009.
5. R. Salomon, Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. a survey of some theoretical and practical aspects of genetic algorithms, ELSEVIER, Biosystems 39, 263278, 1996.
6. S. M. Said, and M. Nakamura, A Hierarchical Hybrid Evolutionary Computation for Continuous Function Optimization, IJNGC, Vol. 3, No 1, 2012.
7. J.C. Bezdek, and N.R. Pal, Some new indexes of cluster validity, IEEE Trans. Syst. Man. Cybern., vol. 28, pp. 301-315,1998.
8. G. W. Milligan, and M.C. Cooper, An examination of procedures for determining the number of clusters in a data set, Psychometrika, vol. 50, pp. 159-179,1985.
9. J. Ocenasek, Parallel estimation of distribution algorithms, Ph.D. thesis, Brno University of Technology, 2002.
10. A. Mendiburu-Alberro, Parallel implementation of estimation of distribution algorithms based on proba-bilistic graphical models. application to chemical calibration models, Ph.D. thesis, The University of the Basque Country, 2006.
11. P. Larranaga, and J. A. Lozano, Estimation of Distribution Algorithms, Kluwer Academic Publishers, 2002.
12. L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.
13. G.W. Milligan, Clustering validation: Results and implications for applied analyses, In P. Arabie, L.J. Hubert and G. De Soete (Eds.), Clustering and Classification, Singapore: World Scientific, pp 341-375, 1996.
14. Ray and R. H. Turi, Determination of number of clusters in K-means clustering and application in colour image segmentation, (invited paper) in N R Pal, A K De and J Das (eds), Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT'99), Calcutta, India, Narosa Publishing House, New Delhi, India, ISBN: 81-7319-347-9, pp 137-143, 27-29 December, 1999.
15. C. Gagne, and M. Parizeau, A Robust Master-Slave Distribution Architecture for Evolutionary Computations, Proc of Genetic and Evolutionary Computation Conference, Late Breaking, pp. 80-87, 2003.