《End-to-end Symbolic Regression with Transformers》这篇文章满足了大众对于机器学习的期待,即给定一个优化问题,大语言模型可以直接输出最优解。 但是,鉴于符号回归(Symbolic Regression)已被证明是一个NP-Hard问题(《Symbolic Regression is NP-hard》),对于NP-Hard问题,大语言模型真的可以直接输出最优解吗? 虽然...
This work describes a novel algorithm for symbolic regression, namely symbolic regression by uniform random global search (SRURGS). SRURGS has only one tuning parameter and is very simple conceptually. The method produces random equations, which is useful for the generation of symbolic regression ben...
This paper tackles the challenge of symbolic regression (SR) with a vast mathematical expression space, where the primary difficulty lies in accurately identifying subspaces that are more likely to contain the correct mathematical expressions. Establishing the NP-hard nature of the SR problem, this ...
A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, comp...
PySR is an open-source tool forSymbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, ...
the model is presumed to be a linear combination of input variables. This is in general not well enough, since the target variable might be dependent on a nonlinear function among input variables. Unlike linear regression, SR allows for the search over a wider space of possible mathematical for...
(GNs or GNNs) can be presented, as they have strong and well-motivated inductive biases that are very well suited to complex problems that can be explained. Symbolic regression is applied to fit the different internal parts of the learned model that operate on a reduced size of representations...
Symbolic regression is np-hard. arXiv 2022, arXiv:2207.01018. [Google Scholar] Nocedal, J.; Wright, S.J. Numerical Optimization, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2006. [Google Scholar] Banzhaf, W.; Nordin, P.; Keller, R.E.; Francone, F.D. Genetic Programming: An ...