[HTML][HTML] Interpretable scientific discovery with symbolic regression: a review
Symbolic regression is emerging as a promising machine learning method for learning
succinct underlying interpretable mathematical expressions directly from data. Whereas it …
succinct underlying interpretable mathematical expressions directly from data. Whereas it …
[HTML][HTML] Contemporary symbolic regression methods and their relative performance
Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …
Symbolic regression via neural-guided genetic programming population seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …
Symformer: End-to-end symbolic regression using transformer-based architecture
Many real-world systems can be naturally described by mathematical formulas. The task of
automatically constructing formulas to fit observed data is called symbolic regression …
automatically constructing formulas to fit observed data is called symbolic regression …
Deep generative symbolic regression
Symbolic regression (SR) aims to discover concise closed-form mathematical equations
from data, a task fundamental to scientific discovery. However, the problem is highly …
from data, a task fundamental to scientific discovery. However, the problem is highly …
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression
Semantic Backpropagation (SB) is a recent technique that promotes effective variation in
tree-based genetic programming. The basic idea of SB is to provide information on what …
tree-based genetic programming. The basic idea of SB is to provide information on what …
Semantic backpropagation for designing search operators in genetic programming
In genetic programming, a search algorithm is expected to produce a program that achieves
the desired final computation state (desired output). To reach that state, an executing …
the desired final computation state (desired output). To reach that state, an executing …
Statistical genetic programming for symbolic regression
In this paper, a new genetic programming (GP) algorithm for symbolic regression problems
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …
Improving generalization of genetic programming for symbolic regression with angle-driven geometric semantic operators
Geometric semantic genetic programming (GP) has recently attracted much attention. The
key innovations are inducing a unimodal fitness landscape in the semantic space and …
key innovations are inducing a unimodal fitness landscape in the semantic space and …