A method for parameter calibration and relevance estimation in evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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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