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The article informs that the cost of optimizing expensive problems is dominated by the number of fitness function evaluations required to reach an acceptable solution. For evolutionary algorithms, various approaches exist to reduce this cost by exploiting knowledge of the history of evaluated points. This knowledge can for instance be used to adapt the recombination and mutation operators in order to sample offspring in a promising areas. Knowledge of past evaluations can also be used to build an empirical model that approximates the fitness function to optimize. The approximation is then used to predict promising new solutions at a smaller evaluation cost than the original problem. The prediction quality generally improves with a growing number of evaluated points in the optimization process.