Approximate Evolution Strategy using Stochastic Ranking

The paper describes the approximation of an evolution strategy using stochastic ranking for nonlinear programming. The aim of the approximation is to reduce the number of function evaluations needed during search. This is achieved using two surrogate models, one for the objective function and another for a penalty function based on the constraint violations. The proposed method uses a sequential technique for updating these models. At each generation the surrogate models are updated and at least one expensive model evaluation is performed. The technique is evaluated for some twenty-four benchmark problems.