Boosted Surrogate Models in Evolutionary Optimization

The paper deals with surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed. Such an extension increases the accuracy of surrogate models, thus also the agreement between results of surrogate modelling and results of the intended optimization of the original objective function. The proposed extension is illustrated on a case study in the area of searching catalytic materials optimal with respect to their behaviour in a particular chemical reaction. A genetic algorithm developed specifically for this application area is employed for optimization, multilayer perceptrons serve as surrogate models, and a method called AdaBoost.R2 is used for boosting. Results of the case study clearly confirm the usefulness of boosting for surro-