Genetic algorithms in chemometrics

• Published in: Journal of chemometrics Vol. 26; no. 6; pp. 345 - 351
• Main Authors: Niazi, Ali, Leardi, Riccardo
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.06.2012; Wiley Subscription Services, Inc
• Subjects: Algorithms; Analytical chemistry; Calibration; Genetic algorithms; molecular modeling; optimization
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.2426

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. The first applications of Genetic Algorithms (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. 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.