In tree-based genetic programming (GP), the most frequent subtrees on later generations are likely to constitute useful partial solutions. This paper investigates the effect of encapsulating such subtrees by representing them as atoms in the terminal set, so that the subtree evaluations can be exploited as terminal data. The encapsulation scheme is compared against a second scheme which depends on random subtree selection. Empirical results show that both schemes improve upon standard GP.
CITATION STYLE
Roberts, S. C., Howard, D., & Koza, J. R. (2001). Evolving modules in genetic programming by subtree encapsulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2038, pp. 160–175). Springer Verlag. https://doi.org/10.1007/3-540-45355-5_13
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