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Symbolic regression via neural networks
16th World Congress on Computational Mechanics and 4th Pan American Congress on Computational Mechanics, 2023Identifying governing equations for a dynamical system is a topic of critical interest across an array of disciplines, from mathematics to engineering to biology. Machine learning—specifically deep learning—techniques have shown their capabilities in approximating dynamics from data, but a shortcoming of traditional deep learning is that there is ...
N. Boddupalli, T. Matchen, J. Moehlis
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2021
This chapter provides a detailed description of different symbolic regression methods. Some methods differ directly in the form of coding, as well as variational methods are based on the principle of small variations of the basic solution. By analogy with deep learning, the technology of the multilayer symbolic regression method is presented.
Askhat Diveev, Elizaveta Shmalko
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This chapter provides a detailed description of different symbolic regression methods. Some methods differ directly in the form of coding, as well as variational methods are based on the principle of small variations of the basic solution. By analogy with deep learning, the technology of the multilayer symbolic regression method is presented.
Askhat Diveev, Elizaveta Shmalko
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Explaining Symbolic Regression Predictions
2020 IEEE Congress on Evolutionary Computation (CEC), 2020The outgrowing application of machine learning methods has raised a discussion in the artificial intelligence community on model transparency. In the center of this discussion is the question of model explanation and interpretability. The genetic programming (GP) community has systematically pointed out as one of the major advantages of GP the fact ...
Renato Miranda Filho +2 more
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Genetic Programming and Evolvable Machines, 2004
Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.
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Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.
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2002
Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic interval-valued data. The present paper compares that approach with several possible alternative models using classical techniques; the symbolic regression approach is preferred. Thence, a regression approach is provided for symbolic histogram-valued data.
Lynne Billard, Edwin Diday
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Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic interval-valued data. The present paper compares that approach with several possible alternative models using classical techniques; the symbolic regression approach is preferred. Thence, a regression approach is provided for symbolic histogram-valued data.
Lynne Billard, Edwin Diday
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Multi Objective Symbolic Regression
2016Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite
C. J. Hinde, N. Chakravorti, A. A. West
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Accuracy in Symbolic Regression
2011This chapter asserts that, in current state-of-the-art symbolic regression engines, accuracy is poor. That is to say that state-of-the-art symbolic regression engines return a champion with good fitness; however, obtaining a champion with the correct formula is not forthcoming even in cases of only one basis function with minimally complex grammar ...
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