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Evolution programming (EP) is an important category of evolutionary algorithms. It relies primarily on mutation operators to search for solutions of function optimization problems (FOPs). Recently a series of new mutation operators have been proposed in order to improve the performance of EP. One prominent example is the fast EP (FEP) algorithm which employs a mutation operator based on the Cauchy distribution instead of the commonly used Gaussian distribution. In this paper, we seek to improve the performance of EP via exploring another important factor of EP, namely, the selection strategy. Three selection rules R1-R3 have been presented to encourage both fitness diversity and solution diversity. Meanwhile, two solution exchange rules R4 and R5 have been introduced to further exploit the preserved genetic diversity. Simple theoretical analysis suggests that through the proper use of R1-R5, EP is more likely to find high-fitness solutions quickly. Our claim has been examined on 25 benchmark functions. Empirical evidence shows that our solution selection and exchange rules can significantly enhance the performance of EP.