Solving Symbolic Regression Problems Using Incremental Evaluation In Genetic Programming

Solving Symbolic Regression Problems Using Incremental Evaluation In Genetic Programming

In this paper, we show some experimental results using incremental evaluation with tree adjoining grammar guided genetic programming (DEVTAG) on two symbolic regression problems, a benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (sawtooth problem). In our pi...

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Hauptverfasser: Hoang Tuan-Hao, McKay, R.I., Essam, D., Nguyen Xuan Hoai
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Australia
Biological system modeling
Digital circuits
Evolution (biology)
Fourier series
Genetic mutations
Genetic programming
Polynomials
Regression tree analysis
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Zusammenfassung:In this paper, we show some experimental results using incremental evaluation with tree adjoining grammar guided genetic programming (DEVTAG) on two symbolic regression problems, a benchmark polynomial fitting problem in genetic programming, and a Fourier series problem (sawtooth problem). In our pilot study, we compare results with standard genetic programming (GP) and the original tree adjoining grammar guided genetic programming (TAG3P). Our results on the two problems are good, outperforming both standard GP and the original TAG3P.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2006.1688570