Abstract
This paper proposes a new tree-generation algorithm
for grammar-guided genetic programming that includes a
parameter to control the maximum size of the trees to
be generated. An important feature of this algorithm is
that the initial populations generated are adequately
distributed in terms of tree size and distribution
within the search space. Consequently, genetic
programming systems starting from the initial
populations generated by the proposed method have a
higher convergence speed. Two different problems have
been chosen to carry out the experiments: a laboratory
test involving searching for arithmetical equalities
and the real-world task of breast cancer prognosis. In
both problems, comparisons have been made to another
five important initialisation methods.
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