《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, ...
of attributes to one another in a feature space and hence they are more likely to be classified in the same class. Linear models such as logistic regression assume that a linear boundary can always separate the classes, although this being a hard bias, as the model cannot learn anything ...
Beham, A. Scheibenpflug, and M. Affenzeller, "Knowledge Discovery Through Symbolic Regression with HeuristicLab," in Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - V. Part II (ECML PKDD'12), 2012, pp. 824-827....
The complexity stems from the dual challenge in symbolic regression (SR) where both the structure and numerical parameters of the symbolic model undergo optimization. Notably, optimizing the structure of symbolic models is recognized as an NP-hard problem, as the latest research suggests [12]. Addi...
Using symbolic regression tools, three different models were produced as follows. 2.1.1. Model 1 of Hydrogen H2 The first developed model is given in Equation (1) 𝐻2(%)=8.9065269419cos(cos(𝑒sin(𝑡+1)−2)). H2(%)=8.9065269419cos(cos(esin(t+1)−2)). (1) This model pe...