Results 271 to 280 of about 17,106 (295)

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
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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
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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
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A Memetic Algorithm for Symbolic Regression

2019 IEEE Congress on Evolutionary Computation (CEC), 2019
This research aims to address the practical difficulties of computational heuristics for symbolic regression, which models data with algebraic expressions. In particular we are motivated by cases in which the target unknown function may be best represented as the ratio of functions.
Sun, Haoyuan, Moscato, Pablo
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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
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Extreme Accuracy in Symbolic Regression

2014
Although recent advances in symbolic regression (SR) have promoted the field into the early stages of commercial exploitation, the poor accuracy of SR is still plaguing even the most advanced commercial packages today. Users expect to have the correct formula returned, especially in cases with zero noise and only one basis function with minimally ...
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Bloat and Generalisation in Symbolic Regression

2014
Symbolic regression is a common application of genetic programming GP. Increasingly, the GP community is identifying the need to measure the generalisation performance of the models evolved in symbolic regression, and consequently the need to design operators and methods that promote generalisation.
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Symbolic regression

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, 2008
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Search based symbolic regression

Im Zentrum der empirischen Wissenschaften steht die Formulierung von Modellen auf Grundlage von Beobachtungsdaten. Solche Modelle müssen sowohl präzise sein, um überprüfbare Vorhersagen zu ermöglichen, als auch interpretierbar, um Einblicke in die zugrunde liegenden Prozesse zu liefern.
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PS-Tree: A piecewise symbolic regression tree

Swarm and Evolutionary Computation, 2022
Hengzhe Zhang, Aimin Zhou, Hong Qian
exaly