Abstract
The use of protected operators and squared error measures are standard approaches in symbolic regression. It will be shown that two relatively minor modifications of a symbolic regression system can result in greatly improved predictive performance and reliability of the induced expressions. To achieve this, interval arithmetic and linear scaling are used. An experimental section demonstrates the improvements on 15 symbolic regression problems.
Keywords
- Mean Square Error
- Genetic Programming
- Interval Arithmetic
- Linear Scaling
- Normalize Root Mean Square Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Keijzer, M. (2003). Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_7
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DOI: https://doi.org/10.1007/3-540-36599-0_7
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