Optimization for black-box objective functions using sensitivity information in SVM

In many practical engineering design problems, the form of objective function is not given explicitly in terms of design variables. Under this circumstance, it usually takes expensive computation time to obtain the value of objective function by some analysis such as structural analysis, fluid mechanic analysis, and so on. In order to make the number of analyses as few as possible, we suggest a method by which optimization is performed in parallel with predicting the form of objective function. In this paper, support vector machine (SVM) is employed in predicting the form of objective function, and genetic algorithms (GA) in searching the optimal value of the predicted objective function.