When distributed data is classified by traditional and single BP algorithm

training speed is low and the local optimum 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 basis 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 convergence 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.