Abstract
Evolution strategies (ESs) constitute a class of engineering optimization algorithms based on the model of natural evolution. ESs are used particularly for real-valued function optimization in which they show better performance than the other evolutionary algorithms in many problems. However, as shown in this paper, their performance dramatically changes according to the lower bound of strategy parameters, although they are traditionally considered to be controlled by a so-called “self-adaptive” property of their own. Therefore, ESs should be applied to each optimization problem with a carefully selected lower bound. In order to overcome this brittleness, this paper proposes a new extended ES called Robust ES (RES). RES adopts redundant individual representation and new mutation mechanisms so that the strategy parameters can be changed by not only their self-adaptive mechanisms based on natural selection but also the effect of genetic drift in their non-coding region. Computer simulations using several test functions are conducted to illustrate the robustness of the proposed approach.
