Abstract
Due to the parameters of traditional support vector machine are difficult to determine in huge information data, so it is seriously limiting its application field. On the basis of classical particle swarm optimization algorithm, this paper proposed an adaptive particle swarm optimization algorithm which can filter the redundant information and adaptive to choose the best parameters in the process of training support vector machine, and the support vector machine using the optimal parameter to establish identification model. In this work, it is found that different insect-resistant genes (Bt-Cry2, Bt-Cry4, Bt-Cry10) are recognized by using this optimization model. Our study provides a precise, fast, convenient and nondestructive detection method for genetically modified organism.
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