GEP is a biologically motivated machine learning technique used to solve complex multitude problems. Similar to other evolution algorithms, GEP is slow when dealing with a large number of population. Considering that the parallel GEP has great efficiency and the niching method can keep diversity in the process of exploring evolution, a niching GEP algorithm based on parallel model is presented and discussed in this paper. In this algorithm, dividing the population to the niche nodes in sub-populations can solves the same problem in less computation time than it would take on a single process. Experimental results on sequence induction, function finding and sunspot prediction demonstrate its advantages and show that the proposed method takes less computation time but with higher accuracy. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Lin, Y., Peng, H., & Wei, J. (2007). A niching gene expression programming algorithm based on parallel model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4847 LNCS, pp. 261–270). Springer Verlag. https://doi.org/10.1007/978-3-540-76837-1_30
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