Evolution Strategies for Robust Optimization

In this paper, we propose two evolutionary strategies for the optimization of problems with actuator noise as encountered in robust optimization, where the design or objective parameters are subject to noise: the ROSAES and the ROCSAES. Both algorithms use a control rule for increasing the population size when the residual error to the optimizer state has been reached. Theoretical analysis has previously shown that the residual error depends among other factors on the population size and on the variance of the noise. Furthermore, ROSAES exploits the similarity of the mutation term in evolutionary strategies and the additive noise term in the case of actuator noise. The population variance is controlled to guarantee that the realized noise level is adjusted correctly. Simulations are carried out on test functions and the results are analyzed with respect to the performance and the dependence of ROSAES and ROCSAES on newly introduced exogenous strategy parameters.