Results 271 to 280 of about 6,391,262 (321)

A New View on Symbolic Regression

2002
Symbolic regression is a widely used method to reconstruct mathematical correlations. This paper presents a new graphical representation of the individuals reconstructed in this process. This new three dimensional representation allows the user to recognize certain possibilities to improve his setup of the process parameters.
Klaus Weinert, Marc Stautner
openaire   +1 more source

Symbolic Regression with a Learned Concept Library

Neural Information Processing Systems
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by inducing a
Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri   +4 more
semanticscholar   +1 more source

Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning

AAAI Conference on Artificial Intelligence
Symbolic regression (SR) has emerged as a pivotal technique for uncovering the intrinsic information within data and enhancing the interpretability of AI models.
Chenglu Sun, Shuo Shen, Wenzhi Tao, Deyi Xue, Zixia Zhou   +4 more
semanticscholar   +1 more source

StruSR: Structure-Aware Symbolic Regression with Physics-Informed Taylor Guidance

arXiv.org
Symbolic regression aims to find interpretable analytical expressions by searching over mathematical formula spaces to capture underlying system behavior, particularly in scientific modeling governed by physical laws.
Yunpeng Gong, Sihan Lan, Can Yang, Kunpeng Xu, Min Jiang   +4 more
semanticscholar   +1 more source

Evolving Equation Learner for Symbolic Regression

IEEE Transactions on Evolutionary Computation
Symbolic regression, a multifaceted optimization challenge involving the refinement of both structural components and coefficients, has gained significant research interest in recent years.
Junlan Dong, Jinghui Zhong, Wei-li Liu, Jun Zhang   +3 more
semanticscholar   +1 more source

A GRASP approach for Symbolic Regression

2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
In this paper a metaheuristic approach is proposed for solving the problem of symbolic regression for function approximation. The focus is on developing a method that is easy to implement and can be used to generate initial populations for more advanced metaheuristics.
Raka Jovanovic, Sahel Ashhab
openaire   +2 more sources

Improving Monte Carlo Tree Search for Symbolic Regression

Neural Information Processing Systems
Symbolic regression aims to discover concise, interpretable mathematical expressions that satisfy desired objectives, such as fitting data, posing a highly combinatorial optimization problem.
Zhengyao Huang, D. Huang, Tiannan Xiao, Dina Ma, Zhenyu Ming, Hao Shi, Yuan-Hui Wen   +6 more
semanticscholar   +1 more source

Multi Objective Symbolic Regression

2016
Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite
Chris J. Hinde, Nandini Chakravorti, Andrew A. West   +2 more
openaire   +2 more sources

Feature Standardisation in Symbolic Regression

2018
While standardisation of variables is a common practice for many machine learning algorithms, it is rarely seen in the literature on genetic programming for symbolic regression. This paper compares the predictive performance of unscaled and standardised genetic programming, using artificial datasets and benchmark problems.
Caitlin A. Owen, Grant Dick, Peter A. Whigham   +2 more
openaire   +1 more source

Symbolic regression in multicollinearity problems

Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005
In this paper the potential of GP-generated symbolic regression for alleviating multicollinearity problems in multiple regression is presented with a case study in an industrial setting. The main advantage of this approach is the potential to produce a simple and stable polynomial model in terms of the original variables.
Flor A. Castillo, Carlos M. Villa
openaire   +2 more sources