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Explainable artificial intelligence by genetic programming: A survey
Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …
to its importance in critical application domains, such as self-driving cars, law, and …
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 …
Interpretable machine learning for science with PySR and SymbolicRegression. jl
PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …
which aims to discover human-interpretable symbolic models. PySR was developed to …
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 …
Symbolicgpt: A generative transformer model for symbolic regression
Symbolic regression is the task of identifying a mathematical expression that best fits a
provided dataset of input and output values. Due to the richness of the space of …
provided dataset of input and output values. Due to the richness of the space of …
Chaos as an interpretable benchmark for forecasting and data-driven modelling
The striking fractal geometry of strange attractors underscores the generative nature of
chaos: like probability distributions, chaotic systems can be repeatedly measured to produce …
chaos: like probability distributions, chaotic systems can be repeatedly measured to produce …
Symbolic physics learner: Discovering governing equations via monte carlo tree search
Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and
engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics …
engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics …
Geometric semantic genetic programming
Abstract Traditional Genetic Programming (GP) searches the space of functions/programs by
using search operators that manipulate their syntactic representation, regardless of their …
using search operators that manipulate their syntactic representation, regardless of their …
Genetic programming needs better benchmarks
Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its
benchmark problems are popular purely through historical contingency, and they can be …
benchmark problems are popular purely through historical contingency, and they can be …
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 …