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Gene expression programming (GEP) is a powerful evolutionary algorithm derived from genetic algorithm and genetic programming for system modeling and knowledge discovery. However, when dealing with complex problems, GEP shows quite slow convergence speed, it also probably encounters premature convergence. This paper proposed a clonal selection- based gene expression programming (CS-GEP), which combines the advantages of clonal selection algorithm (CSA) and GEP, overcoming some drawbacks of GEP. CS-GEP is applied into function modeling experiments, the results show that CS-GEP has faster convergence speed and higher modeling precision than that of GEP.