A Surrogate Assisted Approach for Fitness Computation in Robust Optimization over Time

One of the crucial aspects of solving robust optimization over time (ROOT) problems is to efficiently approximate the robustness of the solutions. However, current progress in this area has been scarce to date. To help bridge this gap, this paper proposes an alternative approach to one of the predominant frameworks in this field. Specifically, we decouple the fit and prediction of future environments that occur for each fitness evaluation by just evaluating previously fitted surrogate models. In this way, we globally approximate the robustness of the solutions by learning fitness functions, rather than point-wise predicting values during the execution of the algorithm. Preliminary results obtained from computational experiments indicate that this approach can achieve significantly superior performances to the existing framework, especially for specific surrogate model configurations. Furthermore, we show that in certain cases where our algorithms are less efficient than the existing approach, such inefficiency is compensated by improvements in error.