On the benefits of populations for noisy optimization

Evolutionary Computation

Volumn 11, Issue 2, 2003, Pages 111-127

  Arnold, Dirk V ^a   Beyer, Hans Georg ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

( , ) ES; Evolution strategies; Gaussian noise; Noise to signal ratio; Population variance

Indexed keywords

POPULATION VARIANCE;

EVOLUTIONARY ALGORITHMS; OPTIMIZATION; SIGNAL TO NOISE RATIO;

GAUSSIAN NOISE (ELECTRONIC);

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; EVOLUTION; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; EVOLUTION; MODELS, GENETIC; MODELS, STATISTICAL;

EID: 0042378954     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/106365603766646799     Document Type: Article

References (17)

1. Arnold, D. V. (2002). Noisy Optimization with Evolution Strategies, Kluwer Academic Publishers, Norwell, Massachusetts.
2. Arnold, D. V. and Beyer, H.-G. (2001). Investigation of the (μ, λ)-ES in the presence of noise. Proceedings of the 2001 IEEE Congress on Evolutionary Computation. Seoul, Korea, May 27-30, pages 332-339.
3. Arnold, D. V. and Beyer, H.-G. (2002a). Local performance of the (1 + 1)-ES in a noisy environment. IEEE Transactions on Evolutionary Computation, 6(1):30-41.
4. Arnold, D. V. and Beyer, H.-G. (2002b). Expected sample moments of concomitants of selected order statistics. Technical Report CI 134/02, SFB 531, University of Dortmund, Dortmund, Germany. Available at http://sfbci.informatik.uni-dortmund.de/home/English/Publications/Reference/ Downloads/AB02b.ps.
5. Beyer, H.-G. (1993). Toward a theory of evolution strategies: Some asymptotical results from the (1 + λ)-theory. Evolutionary Computation, 1(2):165-188.
6. Beyer, H.-G. (1995). Toward a theory of evolution strategies: The (μ, λ)-theory. Evolutionary Computation, 2(4):381-407.
7. Beyer, H.-G. (1999). On the dynamics of EAs without selection. In Banzhaf, W. and Reeves, C., editors, Foundations of Genetic Algorithms 5, pages 5-26, Morgan Kaufmann, San Mateo, California.
8. Beyer, H.-G. (2001). The Theory of Evolution Strategies, Springer Verlag, Berlin, Germany.
9. David, H. A. and Nagaraja, H. N. (1998). Concomitants of order statistics. In Balakrishnan, N. and Rao, C. R., editors, Handbook of Order Statistics 16, pages 487-513, Elsevier, Amsterdam, Netherlands.
10. Miller, B. L. and Goldberg, D. E. (1997). Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 4(2):113-131.
11. Nissen, V. and Propach, J. (1998). Optimization with noisy function evaluations. In Eiben, A. E. et al., editors, Parallel Problem Solving from Nature V, pages 159-168, Springer Verlag, Berlin, Germany.
12. Oyman, A. I., Beyer, H.-G., and Schwefel, H.-P. (2000). Analysis of a simple ES on the "parabolic ridge". Evolutionary Computation, 8(3):249-265.
13. Rattray, M. and Shapiro, J. L. (1997). Noisy fitness evaluation in genetic algorithms and the dynamics of learning. In Belew, R. K. and Vose, M. D., editors, Foundations of Genetic Algorithms 4, pages 117-139, Morgan Kaufmann, San Mateo, California.
14. Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer Systeme nach den Prinzipien der biologischen Evolution, Frommann-Holzboog, Stuttgart, Germany.
15. Rechenberg, I. (1994). Evolutionsstrategie '94, Frommann-Holzboog, Stuttgart, Germany.
16. Schwefel, H.-P. (1995). Evolution and Optimum Seeking, Wiley, New York, New York.
17. Stuart, A. and Ord, J. K. (1994). Kendall's Advanced Theory of Statistics, Sixth edition, Volume I: Distribution Theory, Arnold, London, United Kingdom.