Abstract
Gene expression programming (GEP) is a recently developed evolutionary computation method for model learning and knowledge discovery. Sometimes it is not easy when use GEP to solve too complex problem, so enhancing the algorithm learning capability is necessary. This paper proposes an immune principle based GEP algorithm (iGEP), which combines gene library and clonal selection algorithm. The gene library is composed of subexpressions of GEP expression selected from the process of evolution. The proposed algorithm introduces some new features, including the best subexpression of GEP expression is selected as the solution of the problem, and some segments of gene library are used for hypermutation and receptor editing. In terms of convergence rate and computational efficiency, the experimented results on some benchmark problems of the UCI repository show that iGEP outperforms the standard GEP.
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Xue, S., Wu, J. (2008). Gene Expression Programming Based on Subexpression Library and Clonal Selection. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_6
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DOI: https://doi.org/10.1007/978-3-540-92137-0_6
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