Exploring the mathematic equations behind the materials science data using interpretable symbolic regression
G Wang, E Wang, Z Li, J Zhou… - Interdisciplinary Materials, 2024 - Wiley Online Library
Symbolic regression (SR), exploring mathematical expressions from a given data set to
construct an interpretable model, emerges as a powerful computational technique with the …
construct an interpretable model, emerges as a powerful computational technique with the …
Advancing symbolic regression for earth science with a focus on evapotranspiration modeling
Artificial Intelligence (AI) assumes a pivotal role in Earth science, leveraging deep learning's
predictive capabilities. Despite its prevalence, the impact of AI on scientific discovery …
predictive capabilities. Despite its prevalence, the impact of AI on scientific discovery …
From desolation to preservation: Investigating longitudinal trends in forest coverage and implications for future environmental strategies
Pakistan's forest cover is experiencing significant degradation in the ongoing efforts to
combat climate change. The current state of the climate catastrophe is acknowledged …
combat climate change. The current state of the climate catastrophe is acknowledged …
Subjective social integration and its spatially varying determinants of rural-to-urban migrants among Chinese cities
Q Chen, C Wang, P He, A Cai - Scientific Reports, 2024 - nature.com
Social integration, a huge issue triggered by migration, leads to potential social
fragmentation and confrontation. Focusing on the precise enhancement of" inner" subjective …
fragmentation and confrontation. Focusing on the precise enhancement of" inner" subjective …
Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length
Z Yu, J Ding, Y Li - arXiv preprint arXiv:2411.03753, 2024 - arxiv.org
Symbolic regression, a task discovering the formula best fitting the given data, is typically
based on the heuristical search. These methods usually update candidate formulas to obtain …
based on the heuristical search. These methods usually update candidate formulas to obtain …
The Inefficiency of Genetic Programming for Symbolic Regression
G Kronberger, F Olivetti de Franca, H Desmond… - … Conference on Parallel …, 2024 - Springer
We analyse the search behaviour of genetic programming (GP) for symbolic regression (SR)
in search spaces that are small enough to allow exhaustive enumeration, and use an …
in search spaces that are small enough to allow exhaustive enumeration, and use an …
The Inefficiency of Genetic Programming for Symbolic Regression--Extended Version
G Kronberger, FO de Franca, H Desmond… - arXiv preprint arXiv …, 2024 - arxiv.org
We analyse the search behaviour of genetic programming for symbolic regression in
practically relevant but limited settings, allowing exhaustive enumeration of all solutions …
practically relevant but limited settings, allowing exhaustive enumeration of all solutions …
A comparative study for data approximation between two explainable artificial intelligence approaches
KS Nassrullah, IV Stepanyan, HS Nasrallah… - AIP Conference …, 2024 - pubs.aip.org
Recently, finding the mathematical equations that match with data from any function has
been considered a significant challenge for artificial intelligence and is known as symbolic …
been considered a significant challenge for artificial intelligence and is known as symbolic …
[PDF][PDF] Integrating top-level constraints into a symbolic regression search algorithm
M Djukanovic, A Kartelj - researchgate.net
In this paper we deal with the well-known symbolic regression problem. Previously, we have
proposed the efficient metaheuristic approach called RILS-ROLS for symbolic regression …
proposed the efficient metaheuristic approach called RILS-ROLS for symbolic regression …