Evolution strategies applied to perturbed objective functions

Author

Bäck, Thomas ; Hammel, Ulrich

Author_Institution

Dept. of Comput. Sci., Dortmund Univ., Germany

fYear

1994

fDate

27-29 Jun 1994

Firstpage

40

Abstract

We investigate the behavior of evolution strategies on noisy objective functions. We show for the simple sphere model that convergence velocity is not reduced as long as the noise level is small compared to the function value. If the noise level reaches a certain threshold, a size of the parent population greater than 1 improves the convergence precision significantly. Convergence reliability is tested for two nonconvex functions. Again the search process seems to be not influenced by low level noise. Interpreting the impact of noise purely as a modification of the selection process gives new insight into the role of selection in evolution strategies

Keywords

convergence of numerical methods; genetic algorithms; minimisation; noise; perturbation techniques; search problems; convergence precision; convergence reliability; convergence velocity; evolution strategies; function value; low level noise; noise level; noisy objective functions; nonconvex functions; parent population; perturbed objective functions; search process; simple sphere model; Application software; Computer science; Computer simulation; Convergence; Design optimization; Electronic switching systems; Genetic mutations; Noise level; Noise robustness; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.350045

Filename

350045