Using classification for constrained memetic algorithm: A new paradigm

Regression has been successfully combined with the memetic algorithm (MA) for constructing surrogate models. It is essentially an attempt to approximate the objective or constraint landscape of a constrained optimization problem. Classification, on the other hand, has probably never been thought of being of any assistance to the MA. In fact, it can be used to approximate the feasibility boundary by means of some decision functions. The search effort can thus be focussed on the nearby region, recalling that many constrained optimization problems have their optimal solutions situated on the boundaries. This simply means that only potential individuals will undergo local refinements, reducing the number of function evaluations and accelerating the identification of the global optimum. Presented in this paper is a new approach that combines the support vector machine (SVM) with the MA to achieve this purpose.