A Multi-objective Genetic Algorithm for Model Selection for Support Vector Machines

Selecting the proper Kernel function in SVMs and the specific parameters for that kernel is an important step in achieving a high performance learning machine. The objective of this research is to optimize SVMs parameters using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors and the margin define our objective functions. So, we introduce a method based on multi-objective evolutionary algorithm NSGA-II to solve this problem. We also introduce a multi-criteria selection operator for our NSGA-II. The proposed method is applied on some benchmark datasets. The experimental obtained results show the efficiency of the proposed method.