Initialization method for grammar-guided genetic programming

doi:10.1016/j.knosys.2006.11.006

2007-03-01

PRE

Daniel Manrique *

José Ríos

Alfonso Rodríguez‐Patón

DOI：10.1016/j.knosys.2006.11.006

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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 initialization methods. (c) 2006 Elsevier B.V. All rights reserved.

Keyword:

grammar-guided genetic programming

initialization method

tree-generation algorithm

breast cancer prognosis