Estimation of distribution algorithm based on archimedean copulas

2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09

Volumn , Issue , 2009, Pages 993-996

  Wang, Li Fang ^a,b   Zeng, Jian Chao ^b   Hong, Yi ^a  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

^b TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Archimedean copulas; Copula theory; Estimation of distribution algorithms (EDAs); Sklar's theorem

Indexed keywords

ARCHIMEDEAN COPULA; ARCHIMEDEAN COPULAS; COPULA THEORY; ESTIMATION OF DISTRIBUTION ALGORITHMS; ESTIMATION OF DISTRIBUTION ALGORITHMS (EDAS); JOINT PROBABILITY; MARGINAL DISTRIBUTION; RESEARCH DOMAINS; SKLAR'S THEOREM;

CALCULATIONS; ESTIMATION; EVOLUTIONARY ALGORITHMS; HEURISTIC ALGORITHMS; PARAMETERIZATION; PROBABILITY; SEMICONDUCTING GERMANIUM COMPOUNDS;

DISTRIBUTION FUNCTIONS;

EID: 67650673008     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1543834.1543991     Document Type: Conference Paper

References (20)

1. Larranaga, P., and Lozano, J. A. 2002. Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Boston: Kluwer Academic Publishers.
2. Baluja, S. 1994. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Technical Report CMU-CS-94-163, Pittsburgh, PA: Carnegie Mellon University. URL= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.8084.
3. Sebag, M., and Ducoulombier, A. 1998. Extending population-based incremental learning to continuous search spaces. In Proceedings of the 5th Conference on Parallel Problem Solving from Narute- PPSN V (Amsterdam, Netherlands, September 27-30, 1998 ). Volume 1498 of Lecture Notes in Computer Science. Springer, Berlin, 418-427. URL= http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.42.1884.
4. Muhlenbein, H., and Paass, G. 1996. From recombination of genes to the estimation of distributions I. Binary Parameters. In Proceedings of the 4th Conference on Parallel Problem Solving from Nature- PPSN IV (Berlin, Germany, September 22 - 26, 1996). Volume 1141 of Lecture Notes in Computer Science. Springer, Berlin, 178-187. URL= http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.7.7030&rep=rep1&type=ps.
5. Larranaga, P., Etxeberria, R., Lozano, J. A., and Pena, J. M. 2000. Optimization in continuous domains by learning and simulation of Gaussian networks. In Proceedings of the Genetic and Evolutionary Computation Conference (Las Vegas, Nevada, USA, July 8-12, 2000). GECCO '00 .Morgan Kaufmann, San Francisco, 201-204.
6. De Bonet, J. S., Isbell, C. L. , and Viola, P. 1996. MIMIC: Finding optima by estimation probability densities. Advances in Neural Information Processing Systems , Cambridge: MIT Press, 9:424-430. URL= http://books.nips.cc/ papers/files/nips09/0424.pdf
7. Zhong, W., Liu, J., Liu, F., and Jiao, L. 2004. Second order estimation of distribution algorithms based on kalman filter, Chinese J. Comput., September 2004, 27(9):1272-1277 (in Chinese)
8. Pelikan, M., Goldberg, D. E., and Cantu-Paz, E. 1999. BOA: the Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (Orlando, Florida, USA, July 13-17, 1999,). GECCO 1999, .Morgan Kaufmann, San Francisco, 525-532. URL= http://citeseerx.ist.psu.edu/ viewdoc/download?doi=10.1.1.52.2148&rep=rep1&type=pdf
9. Larranga, P., Etxeberria, R., Lozano, J. A., and Pena, J. M. 1999. Optimization by Learning and Simulation of Bayesian and Gaussian Networks. Technical Report EHU-KZAA-IK-4/99, University of the Basque Country, Spain. URL= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1895
10. Bosman P.A.N. and Thierens D. 2006. Multi-objective optimization with the naive MIDEA. In Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms, J.A. Lozano, P. Larrañaga, I. Inza and E. Bengoetxea, Ed. Springer-Verlag, Berlin, 123-157.URL= http://www.springerlink. com/content/j9n0ul146357r552/
11. Nazan, K., Goldberg, D. E., and Pelikan, M. 2002. Multi-objective Bayesian optimization algorithm. IlliGAL Report No.2002009, University of Illinois at Urbana-Champaign, Urbana, Illinois,
12. Pelikan, M., Sastry, K., and Goldberg, D. E. 2005. Multiobjective hBOA, clustering, and scalability. In Proceedings of the Genetic and Evolutionary Computation Conference (Washington DC, USA, June 25-26, 2005) GECCO'05. ACM Press, New York, 663-670. URL= http://portal.acm.org/citation.cfm?id=1068122.
13. Salhi, A., Rodriguez, J. A. V., and Zhang, Q. 2007. An Estimation of Distribution Algorithm with Guided Mutation for a Complex Flow Shop Scheduling Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (London, England, UK, July 7-11, 2007). ACM Press, New York, 570 - 576. URL= http://portal.acm.org/citation.cfm?id=1276958.1277076
14. Simionescu, P. A., Beale, D. G., and Dozier, G. V. 2006. Teeth-number synthesis of a multispeed planetary transmission using an estimation of distribution algorithm. J. Mech. Design., January 2006, 128(1):108-115.
15. Santarelli, S., Yu, T., Goldberg, D. E., Altshuler, E., O'Donnell, T., and Southall H. 2006. Military antenna design using simple and competent genetic algorithms. Math. Comput. Model., 43(9-10):990-1022
16. Nelsen, R. B. 2006. An Introduction to Copulas (Second Edition). Springer, New York.
17. Demarta, S., and McNeil, A. J. 2007. The t Copula and Related Copulas, Int. Stat. Rev.,73(1):111-129.
18. De Giuli, M. E., Fantazzini, D., and Maggi, M. A. 2008. A New Approach for Firm Value and Default Probability Estimation beyond Merton Models, Comput. Econ., March 2008, 31(2):161-180
19. Cherubini, U., Luciano, E., and Vecchiato, W. 2004. Copula methods in finance. John Wiley.
20. Wang, L. F., Zeng, J. C., and Hong, Y. Estimation of Distribution Based on Copula Theory. In the 2009 IEEE Congress on Evolutionary Computation (Trondheim, Norway, May 18-21, 2009). Paper 622. In press.