A novel multi-strategy DE algorithm for parameter optimization in support vector machine

Support vector machine (SVM) is a powerful technique in pattern classification, but its performance is highly dependent on its parameters. In this paper, a new SVM optimized by a novel differential evolution (DE) with a hybrid parameter setting strategy and a population size adaptation method is proposed and simplified as FDE-PS-SVM. In the hybrid parameter setting strategy, the SVM parameter offspring are generated by DE operators with evolutionary parameters that are fixed or with the ones generated by fuzzy logic inference (FLI) according to a given probability. In the population size adaptation method, the population size is shrunk gradually during the search, which tries to balance the diversity and concentration ability of the algorithm to find better SVM parameters. Some benchmark data sets are used to evaluate the proposed algorithm. Experimental results show that the two proposed strategies are effective to search for better SVM parameters while the proposed FDE-PS-SVM algorithm outperforms other algorithms published in other literature.