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 …
A survey on evolutionary machine learning
Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that
function like humans. AI has been applied to many real-world applications. Machine …
function like humans. AI has been applied to many real-world applications. Machine …
[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 …
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
W Tenachi, R Ibata, FI Diakogiannis - The Astrophysical Journal, 2023 - iopscience.iop.org
Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …
expressions that fit data. While recent advances in deep learning have generated renewed …
Where are we now? A large benchmark study of recent symbolic regression methods
In this paper we provide a broad benchmarking of recent genetic programming approaches
to symbolic regression in the context of state of the art machine learning approaches. We …
to symbolic regression in the context of state of the art machine learning approaches. We …
Artificial intelligence to power the future of materials science and engineering
Artificial intelligence (AI) has received widespread attention over the last few decades due to
its potential to increase automation and accelerate productivity. In recent years, a large …
its potential to increase automation and accelerate productivity. In recent years, a large …
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 …
Parameter identification for symbolic regression using nonlinear least squares
In this paper we analyze the effects of using nonlinear least squares for parameter
identification of symbolic regression models and integrate it as local search mechanism in …
identification of symbolic regression models and integrate it as local search mechanism in …
Demystifying black-box models with symbolic metamodels
AM Alaa, M van der Schaar - Advances in neural …, 2019 - proceedings.neurips.cc
Understanding the predictions of a machine learning model can be as crucial as the model's
accuracy in many application domains. However, the black-box nature of most highly …
accuracy in many application domains. However, the black-box nature of most highly …
Better GP benchmarks: community survey results and proposals
We present the results of a community survey regarding genetic programming benchmark
practices. Analysis shows broad consensus that improvement is needed in problem …
practices. Analysis shows broad consensus that improvement is needed in problem …