Abstract
Genetic Programming (GP) provides evolutionary methods for problems with tree representations. A recent development in Genetic Algorithms (GAs) has led to principled algorithms called Estimation-of-Distribution Algorithms (EDAs). EDAs identify and exploit structural features of a problem’s structure during optimization. Here, we investigate the use of a specific EDA for GP. We develop a probabilistic model that employs transformations of production rules in a context-free grammar to represent local structures. The results of performing experiments on two benchmark problems demonstrate the feasibility of the approach.
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Bosman, P.A.N., de Jong, E.D. (2004). Learning Probabilistic Tree Grammars for Genetic Programming. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_20
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DOI: https://doi.org/10.1007/978-3-540-30217-9_20
Publisher Name: Springer, Berlin, Heidelberg
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