Design of evolutionary algorithms-A statistical perspective

This paper describes a statistical method that helps to find good parameter settings for evolutionary algorithms. The method builds a functional relationship between the algorithm’s performance and its parameter values. This relationship—a statistical model—can be identified thanks to simulation data. Estimation and test procedures are used to evaluate the effect of parameter variation. In addition, good parameter settings can be investigated with a reduced number of experiments. Problem labeling can also be considered as a model variable and the method enables identifying classes of problems for which the algorithm behaves similarly. Defining such classes increases the quality of estimations without increasing the computational cost.