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中文摘要：
为了克服单一BP算法对分布式数据进行分类时具有训练速度陧、易陷入局部最优等缺陷，提出了基于GEP-BP的混合分类算法HCA-GB，同时结合网格服务的思想，提出了基于网格服务的分布式GEP-BP分类算法CDGB-GS，且在HCA-GB算法中，利用自适应系数的方法动态调整GEP种群的大小，从而有效地提高了HCA-GB的全局收敛性．比较仿真实验表明，通过动态调整自适应系数，HCA-GB的平均收敛次数提高了约2倍；对于大数据集而言，在实验室局域网环境下，CDGB-G8算法的平均耗时比传统算法要小，与传统算法相比，CDGB-GS算法的分类精度最大提高了约32．06％．
英文摘要：
When distributed data is classified by traditional and single BP algorithm, training speed is low and the local opti- mum is immersed readily. To solve the problem, in the present research, it presents hybrid classification algorithm based upon GEP-BP （HCA-GB）. In the HCA-GB, range of GEP population is adjusted dynamically by means of self-adaptive coefficient. On the ba- sis of HCA-GB, this paper proposes classification of distributed GEP-BP on grid service （CDGB-GS）, which combines grid service and HCA-GB to resolve classification of distributed data. By simulated experiment, it is showed that the average number of conver- gence of HCA-GB is improved about two times by means of adjusting self-adaptive coefficient. For very large and complex data sets, average consumptive time of CDGB-GS is less than traditional algorithms, and classification accuracy of CDGB-GS is improved by about 32.06 % at most in proportion with traditional algorithms.
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