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Genetic algorithms in chemometrics
Author(s) -
Niazi Ali,
Leardi Riccardo
Publication year - 2012
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2426
Subject(s) - chemometrics , calibration , computer science , genetic algorithm , selection (genetic algorithm) , variable (mathematics) , population , cheminformatics , quality control and genetic algorithms , algorithm , mathematical optimization , biochemical engineering , machine learning , mathematics , chemistry , meta optimization , engineering , statistics , mathematical analysis , demography , sociology , computational chemistry
This review covers the application of Genetic Algorithms (GAs) in Chemometrics. The first applications of GAs in chemistry date back to the 1970s, and in the last decades, they have been more and more frequently used to solve different kinds of problems, for example, when the objective functions do not possess properties such as continuity, differentiability, and so on. These algorithms maintain and manipulate a family, or population, of solutions and implement a “survival of the fittest” strategy in their search for better solutions. GAs are very useful in the optimization and variable selection in modeling and calibration because of the strong effect of the relationship between presence/absence of variables in a calibration model and the prediction ability of the model itself. This review is not a complete summary of the applications of GAs to chemometric problems; its goal is rather to show the researchers the main fields of application of GAs, together with providing a list of references on the subject. Copyright © 2012 John Wiley & Sons, Ltd.