Muhammad Rezaul Karim
ABSTRACT
This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.
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Digital Library
- M. Keijzer. Improving symbolic regression with interval arithmetic and linear scaling. In Proceedings of the 6th European conference on Genetic programming, EuroGP'03, pages 70--82, Berlin, Heidelberg, 2003. Springer-Verlag. Google ScholarDigital Library
- S. Luke. Code growth is not caused by introns. In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, pages 228--235. Morgan Kaufmann, 2000.Google Scholar
Index Terms
- On improving grammatical evolution performance in symbolic regression with attribute grammar
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