Distributed Function Mining for GEP on Grid Services
基于网格服务的GEP分布式函数挖掘算法

This paper presented distributed function mining for GEP on grid services(DFMGEP-GS),which combined grid services and GEP algorithm to realize not only function finding for GEP on grid platform successfully, improve but also global optimization of GEP algorithm with every grid node.Meanwhile, it proved the method by which global data model is obtained by means of local data model on grid.By simulation experiment,it is showed that for data with known function type, and with the augmentation of datasets, average consumptive time of DFMGEP-GS is less than other three algorithms under the condition of mining target function successfully, and that with the increment of grid nodes, the convergent speed of DFMGEP-GS is improved about 17 times maximally.For very complex data with unknown function type, the error which is mined by DFMGEP-GS algorithm is minimum.