Threshold selection, hypothesis tests, and DOE methods

Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002

Volumn 1, Issue , 2002, Pages 777-782

  Beielstein, Thomas ^a   Markon, Sandor ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b Fujitec Research Center Ltd   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DESIGN OF EXPERIMENTS; STOCHASTIC SYSTEMS;

DOE METHODS; HYPOTHESIS TESTING; HYPOTHESIS TESTS; OPTIMAL SELECTION; SELECTION MECHANISM; SIMPLE MODELING; STOCHASTIC SEARCH; THRESHOLD SELECTION;

EVOLUTIONARY ALGORITHMS;

EID: 21044455866     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2002.1007024     Document Type: Conference Paper

References (17)

1. Dirk V. Arnold. Evolution strategies in noisy environments- A survey of existing work. In L. Kallel, B. Naudts, and A. Rogers, editors, Theoretical Aspects of Evolutionary Computing, Natural Computing, pages 239-249. Springer, Berlin, 2001.
2. George E. P. Box and N. R. Draper. Experimental Model Building and Response Surfaces. Wiley, 1987.
3. T. Beielstein. Design of experiments and evolutionary algorithms. Part I: The classical model. Technical report, Universität Dortmund, Fachbereich Informatik, 2001. (to appear).
4. Hans-Georg Beyer. Evolutionary algorithms in noisy environments: Theoretical issues and guidelines for practice. CMAME (Computer methods in applied mechanics and engineering), 186:239-267,2000.
5. George E. P. Box, G. Hunter, William, and John Stuart Hunter. Statistics for experimenters. Wiley series in probabilty and mathematical statistics: Applied probability and statistics. Wiley, 1978.
6. J. Branke, C. Schmidt, andH. Schmeck. Efficient fitness estimation in noisy environments. In L. Spector et al., editor, Genetic and Evolutionary Computation Conference (GECCO '01). Morgan Kaufmann, 2001.
7. David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
8. J. Kleijnen. Statistical Tools for Simulation Practitioners. Marcel Dekker.New York, 1987.
9. J. Kleijnen. Validation of models: Statistical techniques and data availability. In P.A. Farrington, H.B. Nembhard, D.T. Sturrock, and G.W. Evans, editors, Proceedings of the 1999 Winter Simulation Conference, pages 647-654,1999.
10. J. Kleijnen. Experimental designs for sensitivity analysis of simulation models. In A.W. Heemink et al, editor, Proceedings of EUROSIM 2001,2001.
11. Averill M. Law and W. David Kelton. Simulation Modelling and Analysis. McGraw-Hill Series in Industrial Egineering and Management Science. McGraw-Hill, New York, 3 edition, 2000.
12. Sandor Markon, Dirk V. Arnold, Thomas Bäck, Thomas Beielstein, and Hans-Georg Beyer. Thresholding - a selection operator for noisy ES. In J.-H. Kim, B.-T. Zhang, G. Fogel, and I. Kuscu, editors, Proc. 2001 Congress on Evolutionary Computation (CEC'01), pages 465-472, Seoul, Korea, May 27-30, 2001. IEEE Press, Piscataway NJ.
13. Sandor Markon. Studies on Applications of Neural Networks in the Elevator System. PhD thesis, Kyoto University, 1995.
14. Thomas Nagylaki. Introduction to Theoretical Population Genetics. Springer, Berlin, Heidelberg, 1992.
15. Hans-Paul Schwefel. Evolution and Optimum Seeking. Sixth-Generation Computer Technology. Wiley Interscience, New York, 1995.
16. Y. Sano and H. Kita. Optimization of Noisy Fitness Functions by Means of Genetic Algorithms using History of Search. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature (PPSN VI), volume 1917 of LNCS, pages 571-580. Springer, 2000.
17. Peter Stagge. Averaging efficiently in the presence of noise. In A.Eiben, editor, Parallel Problem Solving from Nature, PPSN V, pages 188-197, Berlin, 1998. Springer-Verlag.