Improving Symbolic Regression with Interval Arithmetic and Linear Scaling

By Maarten Keijzer. European Conference on Genetic Programming, 2003

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Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming (2009, methodology): “The choice of several target expressions was inspired by [42], which introduced 13 functions to analyze the performance of scaled GP.”
Interpretable scientific discovery with symbolic regression: a review (2022, neutral): “In symbolic regression case, the encoder and the decoder do not share the same vocabulary because the decoder has a mixture of symbolic and numeric representations, while the encoder has only numeric representations.”
Semantically-based crossover in genetic programming: application to real-valued symbolic regression (2011, methodology): “By contrast, Keijzer [33] used interval analysis to check whether an individual is defined over the whole range of input values—if an individual is undefined anywhere, that individual can be assigned minimal fitness or simply deleted from the population.”