Turning high-dimensional optimization into computationally expensive optimization

Divide-and-Conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple lowdimensional sub-problems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and sub-problems makes it non-trivial to precisely assess the quality of a candidate solution to a sub-problem, which has been a major hurdle for applying the idea of DC to non-separable highdimensional optimization problems. In this paper, we suggest that searching a good solution to a sub-problem can be viewed as a computationally expensive problem and can be addressed with the aid of meta-models. As a result, a novel approach, namely Self-Evaluation Evolution (SEE) is proposed. Empirical studies have shown the advantages of SEE over 4 representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analysed in the empirical studies.