On the Quality Gain of (1, lambda)-ES Under Fitness Noise

In optimization tasks that deal with real-world applications noise is very common leading to degradation of the performance of Evolution Strategies. We will consider the quality gain of an (1,λ)-ES under noisy fitness evaluations for arbitrary fitness functions. The equation developed will be applied to several test functions to check its predictive quality.

[1]  Hans-Georg Beyer,et al.  Predicting the Solution Quality in Noisy Optimization , 2004 .

[2]  Volker Nissen,et al.  On the robustness of population-based versus point-based optimization in the presence of noise , 1998, IEEE Trans. Evol. Comput..

[3]  Hans-Georg Beyer,et al.  On the Benefits of Populations for Noisy Optimization , 2003, Evolutionary Computation.

[4]  Hans-Georg Beyer,et al.  Investigation of the (/spl mu/, /spl lambda/)-ES in the presence of noise , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[5]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[6]  Dirk V. Arnold,et al.  Noisy Optimization With Evolution Strategies , 2002, Genetic Algorithms and Evolutionary Computation.

[7]  Hans-Georg Beyer,et al.  Local performance of the (1 + 1)-ES in a noisy environment , 2002, IEEE Trans. Evol. Comput..

[8]  John J. Grefenstette,et al.  Genetic algorithms in noisy environments , 1988, Machine Learning.

[9]  Brad L. Miller,et al.  Noise, sampling, and efficient genetic algorthms , 1997 .