关键词: 遗传算法, 人工蜂群算法, 融合, 支持向量机, 参数优化

Abstract: Aiming at the problems that genetic algorithm can’t make good use of system output information, it’s easily to fall into prematurity, and artificial bee colony algorithm is slow at the beginning of the search, this paper combines improved genetic algorithm and artificial bee colony algorithm to achieve?complementary, and takes the classification accuracy of SVM test set as fitness?function to propose Genetic-Artificial Bee Colony Algorithm（G-ABCA） of support vector machine parameter optimization, and compares G-ABCA with improved genetic algorithm and artificial bee colony algorithm by simulation?experiment. The experiment results show that the performance of G-ABCA in support vector machine parameter optimization is better than other algorithms, G-ABCA greatly improves the classification accuracy of SVM, and it is step-by-step convergent.

Key words: genetic algorithm, artificial bee colony algorithm, combination, support vector machine, parameter optimization