Cross entropy and adaptive variance scaling in continuous EDA

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 609-616

  Cai, Yunpeng ^a   Sun, Xiaomin ^a   Xu, Hua ^a   Jia, Peifa ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Adaptive variance scaling; Cross entropy; Estimation of distirbution algorithms; Evolutionary computation

Indexed keywords

ADAPTIVE SYSTEMS; DISTRIBUTION FUNCTIONS; MODEL CHECKING; PARAMETER ESTIMATION; POPULATION STATISTICS;

ADAPTIVE VARIANCE SCALING; CROSS ENTROPY; ESTIMATION OF DISTIRBUTION ALGORITHMS;

EVOLUTIONARY ALGORITHMS;

EID: 34548056762     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277081     Document Type: Conference Paper

References (29)

1. C. Ahn, R. Ramakrishna, and D. Goldberg. Real-coded bayersian optimization algorithm:bringing the strength of boa into the continuous world. In Proceedings of the GECCO-2004 Conference on Genetic and Evolutionary Computation, pages 840-851. Springer Verlag, 2004.
2. A. Berny. Statistical Machine Learning and Combinatorial Optimization. In Theoretical Aspects of Evolutionary Computation, pages 287-306. Springer Verlag, 2001.
3. P. Bosman and J. Grahl. Matching inductive search bias and problem structure in continuous estimation of distribution algorithms. Technical Report 03/2005, Dept. of Logistics, Mannheim Business School, 2005.
4. P. Bosman and D. Thierens. Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA. In Parallel Problem Solving from Nature - PPSN VI, pages 767-776, Paris, France, Sep. 2000. Springer-Verlag.
5. Y. Cai, X. Sun, and P. Jia. Probabilistic Modeling for Continuous EDA with Boltzmann Selection and Kullback-Leibeler Divergence. In Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pages 389-396. ACM Press, 2006.
6. T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, 1991.
7. M. Gallagher and M. Frean. Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift. Evolutionary Computation, 13(1):29-42, 2005.
8. C. González, J. Lozano, and P. Larrañaga. Mathematical Modelling of UMDAc Algorithm with Tournament Selection: Behaviour on Linear and Quadratic Functions. International Journal of Approximate Reasoning, 31(2):313-340, 2002.
9. J. Grahl, P. Bosman, and F. Rothlauf. The Correlation-Triggered Adaptive Variance Scaling IDEA. In Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pages 397-404. ACM Press, 2006.
10. J. Grahl, S. Minner, and F. Rothlauf. Behaviour of UMDAc with Truncation Selection on Monotonous Functions. In The 2005 IEEE Congress on Evolutionary Computation, pages 2553-2559, Edinburgh, Scotland, 2005. IEEE.
11. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolutionary strategiess. Evolutionary Computation, 9(2):159-195, 2001.
12. P. Larrañaga, R. Etxeberria, J. A. Lozano, and J. M. Peña. Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks. In Proceedings of the GECCO-2000 Workshop in Optimization by Building and Using Probabilistic Models, pages 201-204, San Francisco, CA, USA, 2001. Morgan Kaufmann.
13. P. Larrañaga and J. Lozano. Estimation of Distribution Algorithms, A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2002.
14. P. Larrañaga, J. A. Lozano, and E. Bengoetxea. Estimation of Distribution Algorithms based on Multivariate Normal and Gaussian Networks. Technical Report EHU-KZAA-IK-1-01, University of the Basque Country, 2001.
15. J. Lozano, P. Larrañaga, I. Inza, and E. Bengoetxea. Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms. Springer, 2006.
16. H. Mühlenbein. The Equation for Response to Selection and Its Use for Prediction. Evolutionary Computation, 5(3):303-346, 1997.
17. H. Mühlenbein and R. Höns. The Estimation of Distributions and the Minimum Relative Entropy Principle. Evolutionary Computation, 13(1):1-27, 2005.
18. J. Oceanasek and J. Schwarz. Estimation of Distribution Algorithm for Mixed Continuous-Discrete Optimization Problems. In 2nd Euro-International Symposium on Computational Intelligence, pages 227-232, Kosice, Slowakia, 2002. IOS Press.
19. J. Ocenasek, S. Kern, N. Hansen, S. Müller, and P. Koumoutsakos. A Mixed Bayesian Optimization Algorithm with Variance Adaptation. In Parallel Problem Solving from Nature - PPSN VIII, pages 352-361. Springer Verlag, 2004.
20. A. Ostermeier, A. Gawelczyk, and N. Hansen. A derandomized approach to self adaptation of evolution strategies. Evolutionary Computation, 2 (4): 369-380, 1994.
21. M. Pelikan, D. Goldberg, and S. Tsutsui. Getting the Best of Both Worlds: Discrete and Continuous Genetic and Evolutionary Algorithms in Concert. Information Sciences, 156(3-4):147-171, 2003.
22. M. Pelikan, S. K., and C.-P. E. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2006.
23. I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.
24. S. Rudolf and M. Köppen. Stochastic Hill Climbing by Vectors of Normal Distributions. In Proceedings of the First Online Workshop on Soft Computing, Nagoya, Japan, 1996.
25. K. S. Information theory and statistics. John Wiley and Sons, 1959.
26. H.-P. Schwefel. Numerical Optimization of Computer Models. Wiley, 1981.
27. M. Sebag and A. Ducoulombier. Extending Population-Based Incremental Learning to Continuous Search Spaces. In Parallel Problem Solving from Nature - PPSN V., pages 418-427, Amsterdam, Netherlands, Sep. 1998. Springer-Verlag.
28. S. Shin and B.-T. Zhang. Bayesian Evolutionary Algorithms for Continuous Function Optimization. In Proc. 2001 Congress on Evolutionary Computation, pages 508-515. IEEE, 2001.
29. B. Yuan and M. Gallagher. On the Importance of Diversity Maintenance in Estimation of Distribution Algorithms. In Proceedings of the 7th Conference on Genetic and Evolutionary Computation, pages 719-726. ACM Press, 2005.