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Population-based evolutionary algorithms are often considered whenapproaching computationally expensive black-box optimization problems. Theyemploy a selection mechanism to choose the best solutions from a givenpopulation after comparing their objective values, which are then used togenerate the next population. This iterative process explores the solutionspace efficiently, leading to improved solutions over time. However, thesealgorithms require a large number of evaluations to provide a quality solution,which might be computationally expensive when the evaluation cost is high. Insome cases, it is possible to replace the original objective function with aless accurate approximation of lower cost. This introduces a trade-off betweenthe evaluation cost and its accuracy.  In this paper, we propose a technique capable of choosing an appropriateapproximate function cost during the execution of the optimization algorithm.The proposal finds the minimum evaluation cost at which the solutions are stillproperly ranked, and consequently, more evaluations can be computed in the sameamount of time with minimal accuracy loss. An experimental section on four verydifferent problems reveals that the proposed approach can reach the sameobjective value in less than half of the time in certain cases.

Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems