Clone selection programming and its application to symbolic regression

A new idea 'clone selection programming (CSP)' is introduced in this paper. The proposed methodology is used for deriving new algorithms in the area of evolutionary computing aimed at solving a wide range of problems. In CSP, antibodies represent candidate solutions, which are encoded according to the structure of antibody. The antibodies are able to keep syntax correct even they are changed with iterations. Also, the clone selection principle is developed as a search strategy. The proposed strategies have been thoroughly evaluated by intensive simulations. The results demonstrate the effectiveness and excellent convergent qualities of the CSP based search strategy. In our study, the convergence rate with respect to population size and other parameters is studied. A thorough comparative study between our proposed CSP based method with the gene expression programming (GEP), and immune programming (IP) are included. The comparative results show that the CSP based method can significantly improve the program performance. The experimental results indicate that the proposed method is very robust under all the investigated cases.

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