Offline Data Driven Evolutionary Optimization Based on Pruning Stacked Generalization

In the real world, there are many complex optimization problems in which the objective function is time-consuming or even unavailable. Traditional optimization methods are either unable to start or inefficient in solving such problems. Offline data driven evolutionary optimization method is no need to evaluate the real objective function in the process of evolutionary, which greatly promotes the solution of expensive optimization problems and unmodeled optimization problems. However, the effectiveness of offline data driven evolutionary optimization depends heavily on surrogate model. In order to improve the quality of surrogate model, this paper proposes a surrogate model construction method based on pruning stack generalization (SG). Specifically, on the one hand, the primary model pool is established based on heterogeneous base learners, and then the primary models are conducted by learning methods to improve the generality and accuracy of surrogate model. On the other hand, the primary models are pruned based on the ranking preservation indicator, which can not only improve the ensemble efficiency of the primary models, but also further improve the accuracy of the final surrogate model, and better guide the evolutionary search. In order to verify the effectiveness of the proposed method, it is compared with 7 state-of-the-arts offline data driven evolutionary optimization algorithms on 12 benchmark problems. The experimental results demonstrate the superior performance of proposed method over compared algorithms. © 2023 Science Press. All rights reserved.