Estimation of fitness landscape contours in EAs

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 562-569

  Poík, Petr ^a   Franc, Vojtch ^b  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^b FRAUNHOFER FIRST   (Germany)

Author keywords

Estimation of distribution algorithms; Evolutionary computation; Function optimization; Learnable evolution model; Separating ellipsoid

Indexed keywords

DISTRIBUTION FUNCTIONS; FUNCTION EVALUATION; LEARNING SYSTEMS; MODEL CHECKING; PARAMETER ESTIMATION;

ESTIMATION OF DISTRIBUTION ALGORITHMS; EVOLUTIONARY COMPUTATIONS; FUNCTION OPTIMIZATION; LEARNABLE EVOLUTION MODELS; SEPARATING ELLIPSOIDS;

EVOLUTIONARY ALGORITHMS;

EID: 34548090273     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277075     Document Type: Conference Paper

References (16)

1. A. Auger, M. Schoenauer, and N. Vanhaecke. LS-CMA-ES: A second-order algorithm for covariance matrix adaptation. In X. Y. et al., editor, Parallel Problem Solving from Nature VIII, number 3242 in LNCS, pages 182-191. Springer Verlag, 2004.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press.
3. P. A. Bosman and J. Grahl. Matching inductive bias and problem structure in continuous estimation-of-distribution algorithms. European Journal of Operational Research, 2006.
4. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
5. N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159-195, 2001.
6. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer series in statistics. Springer Verlag, 2001.
7. P. Larrañaga and J. A. Lozano, editors. Estimation of Distribution Algorithms. GENA. Kluwer Academic Publishers, 2002.
8. R. S. Michalski. Learnable evolution model: Evolutionary processes guided by machine learning. Machine Learning, 38:9-40, 2000.
9. R. S. Michalski. Attributional calculus: A logic and representation language for natural induction. Reports of the Machine Learning and Inference Laboratory MLI 04-2, George Mason University, Fairfax, VA, April 2004.
10. P. Pošík. Real-parameter optimization using the mutation step co-evolution. In IEEE Congress on Evolutionary Computation, pages 872-879. IEEE, 2005. ISBN 0-7803-9364-3.
11. G. Rudolph. Self-adaptive mutations may lead to premature convergence. IEEE Trans. on Evolutionary Computation, 5(4):410-413, August 2001.
12. M. I. Schlesinger and V. Hlaváč. Ten Lectures on Statistical and Structural Pattern Recognition. Kluwer Academic Publishers, Dodrecht, The Netherlands, 2002.
13. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, Massachusetts, 2002.
14. H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995.
15. V. Vapnik and A. Lerner. Pattern recognition using generalized portrait method. Automation and Remote Control, 24:774-780, 1963.
16. J. Wojtusiak and R. S. Michalski. The LEM3 system for non-darwinian evolutionary computation and its application to complex function optimization. Reports of the Machine Learning and Inference Laboratory MLI 04-1, George Mason University, Fairfax, VA, February 2006.