The classification accuracy of support vector machines(SVM) depends on feature selection and parameter
optimization of SVM strongly. The asymptotic behaviors of support vector machines are fused with genetic algorithm
and the feature chromosomes are generated, which directs the search of genetic algorithm to the straight line of optimal
generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature
chromosomes is proposed to simultaneously optimize the feature subset and the parameters for SVM. Compared with the
grid search, the genetic algorithm without feature chromosomes and other approaches, the proposed approach has higher
classification accuracy, smaller feature subset and fewer processing time.