Genetic algorithms in chemometrics and chemistry: a review

By R. Leardi. 2001

397 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/56b704bfd8c21ed812407a88ee0b80fcc9d4e86c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Alejandro C. Olivieri
2. Rasmus Bro
3. Kamel Mansouri
4. José Manuel Amigo
5. M. Vidal
6. Richard S. Judson
7. Christopher M. Grulke
8. Antony J. Williams

In Computational Theory and Mathematics

1. M. Vidal
2. José Manuel Amigo
3. Christopher M. Grulke
4. Richard S. Judson
5. Kamel Mansouri
6. Antony J. Williams
7. Davide Ballabio
8. Thomas Skov

In Spectroscopy

1. M. Vidal
2. José Manuel Amigo
3. Rasmus Bro
4. Richard S. Judson
5. Christopher M. Grulke
6. Davide Ballabio
7. Kamel Mansouri
8. Antony J. Williams

Citations in the corpus

• A new family of genetic algorithms for wavelength interval selection in multivariate analytical spectroscopy (2003, background): “Overfitting is another well-known risk in GA-based sensor selection [12].”
• Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques (2008, methodology): “Concerning GAs [23], the approach used in this paper is an evolution of the algorithm described by Leardi and Lupianez Gonzalez [24], where the basic difference is that GAs are coupled directly with LDA [25].”
• A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, methodology): “a global optimization of feature subset and classifier coefficients [11]), or by statistical tests on the outcome of a learning algorithm (e.”
• OPERA models for predicting physicochemical properties and environmental fate endpoints (2018, methodology): “This procedure has been shown to minimize the risk of overfitting [58, 61, 112].”
• Pre-processing of hyperspectral images. Essential steps before image analysis (2012, methodology): “In this sense, different mathematical strategies, like genetic algorithms [19] or interval PLS (iPLS) [20] allow variable selection in a very efficient way.”
• Classification of blue pen ink using infrared spectroscopy and linear discriminant analysis (2013, background): “The genetic algorithm (GA) is a stochastic search technique that mimics the process of natural evolution [26,27].”