Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms

A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution? We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.