Approximating the search distribution to the selection distribution in EDAs

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 461-468

  Valdez Peña, S Ivvan ^a   Hernández Aguirre, Arturo ^a   Botello Rionda, Salvador ^a  

^a CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS   (Mexico)

Author keywords

Estimation distribution algorithms; Selection methods

Indexed keywords

BEST APPROXIMATIONS; DISTRIBUTION ALGORITHMS; DISTRIBUTION EQUATIONS; ESTIMATION OF DISTRIBUTION ALGORITHMS; FINITE POPULATION; NEW APPROACHES; REAL VARIABLES; SELECTION METHODS; SELECTION OPERATORS; SOURCE DATA; THEORETICAL RESULT;

ESTIMATION; EVOLUTIONARY ALGORITHMS; MATHEMATICAL OPERATORS; POPULATION STATISTICS;

LEARNING ALGORITHMS;

EID: 72749109122     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1569965     Document Type: Conference Paper

References (21)

1. S. Baluja. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning. Technical report, Carnegie Mellon University, Pittsburgh, PA, USA, 1994.
2. P. A. N. Bosman, J. Grahl, and F. Rothlauf. SDR: A Better Trigger for Adaptive Variance Scaling in Normal EDAs. In GECCO '07: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 516-522. ACM, 2007.
3. P. A. N. Bosman and D. Thierens. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA. In PPSN VI: Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, pages 767-776, London, UK, 2000. Springer-Verlag.
4. G. Cooper and E. Herskovits. A Bayesian Method for the Induction of Probabilistics Networks from Data. Machine Learning, 9:309-347, 1992.
5. B. Efron and R. J.Tibshirani. An Introduction to the Bootstrap. Chapman and Hall,Monographs on Statistics and Applied Probability, New York, 1993.
6. J. Grahl, P. A. Bosman, and F. Rothlauf. The Correlation-Triggered Adaptive Variance Scaling IDEA. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 397-404, New York, NY, USA, 2006. ACM.
7. J. Grahl, P. A. N. Bosman, and S. Minner. Convergence Phases, Variance Trajectories, and Runtime Analysis of Continuos EDAs. In GECCO '07: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 516-522. ACM, 2007.
8. P. Larrañaga, R. Etxeberria, J. Lozano, and J. Peña. Optimization by Learning and Simulation of Bayesian and Gaussian Networks. Technical Report EHU-KZAA-IK-4/99, Department of Computer Science and Artificial Intelligence, University of the Basque Country, 1999.
9. P. Larrañaga and J. A. Lozano. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Norwell, MA, USA, 2001.
10. J. A. Lozano, P. Larrañaga, I. Inza, and E. Bengoetxea, editors. Towards a New Evolutionary Computation, volume 192 of Studies in Fuzziness and Soft Computing. Springer, 2006.
11. 2. From Recombination of Genes to the Estimation of Distributions II. Continuous Parameters, 1996.
12. H. Mühlenbein. The Equation for Response to Selection and Its Use for Prediction. Evolutionary Computation, 5(3):303-346, 1997.
13. H. Mühlenbein and G. Paaβ. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. In PPSN IV: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, pages 178-187, London, UK, 1996. Springer-Verlag.
14. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz. BOA: The Bayesian Optimization Algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, volume I, pages 525-532, Orlando, FL, 1999. Morgan Kaufmann Publishers, San Fransisco, CA.
15. M. Pelikan and H. Mühlenbein. The Bivariate Marginal Distribution algorithm. Advances in Soft Computing Engineering Design and Manufacturing, pages 521-535, 1999.
16. M. Pelikan, K. Sastry, and E. Cantú-Paz, editors. Scalable Optimization via Probabilistic Modeling, volume 33 of Studies in Computational Intelligence. Springer, 2006.
17. M. Pelikan, K. Sastry, and D. E. Goldberg. iBOA: The Incremental Bayesian Optimization Algorithm. In GECCO '08: Proceedings of the 2008 Conference on Genetic and Evolutionary Computation, pages 455-462. ACM, 2008.
18. M. Soto and A. Ochoa. A Factorized Distribution Algorithm based on Polytrees. In Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), pages 232 - 237. IEEE, 2000.
19. S. Tsutsui, M. Pelikan, , and D. E. Goldberg. Evolutionary Algorithm using Marginal Histogram Models in Continuous Domain. Technical Report 2001019, Illinois Genetic Algorithms Laboratory, 2001.
20. C. Yunpeng, S. Xiaomin, and J. Peifa. Probabilistic Modeling for Continuous EDA with Boltzmann Selection and Kullback-Leibeler divergence. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 389-396, New York, NY, USA, 2006. ACM.
21. Q. Zhang and H. Mühlenbein. On the Convergence of a Class of Estimation of Distribution Algorithms. IEEE Transactions on Evolutionary Computation, 8(2):127-136, April 2004.