Abstract
In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three gp variants.
Cite
CITATION STYLE
Eggermont, J., & Van Hemert, J. I. (2001). Adaptive genetic programming applied to new and existing simple regression problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2038, pp. 23–35). Springer Verlag. https://doi.org/10.1007/3-540-45355-5_3
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