A Novel Spatial Interpolation Parallel Algorithm based on Genetic Expression Programming

Because of the limited environmental monitoring stations, the spatial data we have obtained by monitoring is local, discrete and limited. Therefore, speculating the environmental pollution parameters without monitoring station by using known monitoring data for spatial interpolation has become a hot research topic. In our preliminary work, an approach combining Genetic Expression Programming (GEP) evolution modeling with Delaunay, is presented to achieve automatic spatial interpolation. However, as a kind of genetic, GEP has the problem of premature convergence. In this paper, a GEP combining parallel algorithm based on MPI is proposed. By adopting the master-slave coarseness parallel GEP algorithm for spatial interpolation and introducing the subgroups migrating policy based on ring topology, this proposed algorithm has realized the coarseness scalable parallel computing which can run in the processor within a certain size to raise the ability of jumping out of the local optimum thus can avoid the problem of premature convergence efficiently. The result of simulation experiment shows that, as to the normal dataset and abnormal dataset of SIC2004, the GEP parallel algorithm based on MPI has the same predictable accuracy as the serial algorithms do, and the execution time is much less than serial algorithm, which can better satisfy the real-time demand of monitoring the content of radioactive material.