ABSTRACT
Traditional approaches to symbolic regression require the use of protected operators, which can lead to perverse model characteristics and poor generalisation. In this paper, we revisit interval arithmetic as one possible solution to allow genetic programming to perform regression using unprotected operators. Using standard benchmarks, we show that using interval arithmetic within model evaluation does not prevent invalid solutions from entering the population, meaning that search performance remains compromised. We extend the basic interval arithmetic concept with 'safe' search operators that integrate interval information into their process, thereby greatly reducing the number of invalid solutions produced during search. The resulting algorithms are able to more effectively identify good models that generalise well to unseen data.
- Grant Dick. 2015. Improving Geometric Semantic Genetic Programming with Safe Tree Initialisation. In European Conference on Genetic Programming. Springer International Publishing, 28--40.Google Scholar
- Grant Dick. 2017. Interval Arithmetic and Interval-Aware Operators for Genetic Programming. (2017). arXiv:arXiv:1704.04998Google Scholar
- Maarten Keijzer. 2003. Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. In Genetic Programming, Conor Ryan, Terence Soule, Maarten Keijzer, Edward Tsang, Riccardo Poli, and Ernesto Costa (Eds.). Lecture Notes in Computer Science, Vol. 2610. Springer Berlin Heidelberg, 70--82. Google ScholarDigital Library
- John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA. Google ScholarDigital Library
Index Terms
- Revisiting interval arithmetic for regression problems in genetic programming
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