Abstract
In this paper the problem of averaging because of noise on the fitness function is addressed. In simulations noise is mostly restricted to the finite precision of the numbers and can often be neglected. However in biology fluctuations are ubiquitous and also in real world applications, where evolutionary methods are used as optimization tools, the presence of noise has to be coped with [1]. This article originated from the second point: Optimizing the structure of Neural Networks their fitness is the result of a learning process. This value depends on the stochastic initialization of the connection strengths and thus represents a noisy fitness value. To reduce noise one can average over several evaluations per individual which is costly. The aim of this work is to introduce a method to reduce the number of evaluations per individual.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
S. Rana, L.D. Whitley, and R. Cogswell. Searching in the Presence of Noise. In Parallel Problem Solving from Nature,4, pages 198–207. Springer Verlag, 1996.
H. Abarbanel, R. Brown, J. Sidorowich, and L. Tsimring. Analysis of observed chaotic data in physical systems. Rev. Mod. Phys., 65:1331–1392, 1993.
Simon Haykin. Neural Networks. Macmillan College Publishing Company, New York, 1994.
Thomas Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996.
Ingo Rechenberg. Evolutionsstrategie '94. Friedrich Frommann Holzboog Verlag, Stuttgart, 1994.
Hans-Georg Beyer. Toward a Theory of Evolution Strategies: Some Asymptotical Results from (1,+ λ)-theory. Evolutionary Computation, 1(2):165–188, 1993.
B. Miller and D. Goldberg. Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise. Evolutionary Computation, 4(2):113–131, 1997.
Ulrich Hammel and Thomas Bäck. Evolution Strategies on Noisy Functions How to Improve Convergence Properties. In Y. Davidor, H.P. Schwefel, and R. Manner, editors, Parallel Problem Solving from Nature, 3, pages 159–168. Springer Verlag, 1994.
Herbert Büning and Götz Trenkler. Nichtparametrische statistische Methoden. Walter de Gruyter, Berlin, 1994.
Bernhard Sendhoff, Martin Kreutz, and Werner von Seelen. A condition for the genotype-phenotype mapping: Causality. In Thomas Bäck, editor, International Conference on Genetic Algorithms. Morgan Kaufman, July 1997.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stagge, P. (1998). Averaging efficiently in the presence of noise. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056862
Download citation
DOI: https://doi.org/10.1007/BFb0056862
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65078-2
Online ISBN: 978-3-540-49672-4
eBook Packages: Springer Book Archive