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Journal ArticleDOI

New applications of the genetic algorithm for the interpretation of high-resolution spectra

W. Leo Meerts, +2 more
- 01 Jun 2004 - 
- Vol. 82, Iss: 6, pp 804-819
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TLDR
Hageman et al. as discussed by the authors proposed a GA-based method for rotationally resolved laser-induced fluorescence spectrum fitting, which can be performed in less than 1h.
Abstract
Rotationally resolved electronic spectroscopy yields a wealth of information on molecular structures in different electronic states. Unfortunately, for large molecules the spectra get rapidly very congested owing to close-lying vibronic bands, other isotopomers with similar zero-point energy shifts, or large-amplitude internal motions. A straightforward assignment of single rovibronic lines and, therefore, line position assigned fits are impossible. An alternative approach is unassigned fits of the spectra using genetic algorithms (GAs) with special cost functions for evaluation of the quality of the fit. This paper decribes the improvements we established on the GA method discussed before (J.A. Hageman, R. Wehrens, R. de Gelder, W.L. Meerts, and L.M.C. Buydens. J. Chem. Phys. 113, 7955 (2000)). In particular, we succeeded in obtaining a dramatic reduction in computing time that made it possible to apply the GA process in a large number of cases. A completely automated fit of a rotationally resolved laser-induced fluorescence spectrum without any prior knowledge of the molecular parameters can now be performed in less than 1 h. We demonstrate the power of the method on a number of typical examples such as very dense rovibronic spectra of van der Waals clusters and overlapping spectra due to different isotopomers. The discussed results demonstrate the extreme power of the GA in automated fitting and assigning of complex spectra. It opens the road to the analysis of complex spectra of biomolecules and their building blocks.

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Citations
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Journal ArticleDOI

Application of genetic algorithms in automated assignments of high-resolution spectra

TL;DR: In this paper, an automated assignment and fitting procedure for high-resolution rotationally resolved spectra is described based on the application of GA and both frequency and intensity information of these spectra are used.
Journal ArticleDOI

Structural Selection by Microsolvation: Conformational Locking of Tryptamine

TL;DR: Ab initio calculations confirm much larger energy differences between the conformers of the water complex than between those of the monomers.
Journal ArticleDOI

AUTOFIT, an automated fitting tool for broadband rotational spectra, and applications to 1-hexanal

TL;DR: In this paper, an automated spectral assignment program called AUTOFIT has been developed to analyze the complex spectra from these broadband measurements, and its performance is illustrated by the analysis of the CP-FTMW spectrum of 1-hexanal obtained over the spectral range 6-40 GHz.
Journal ArticleDOI

Improved parametric time warping for proteomics

TL;DR: An improved version of parametric time warping is presented, which enables the method to be used in LC-MS measurements in proteomics and includes a new similarity measure for comparing warped chromatograms, an insurance against peaks at the extremes of the chromatogram disappearing because of the warping, and the possibility to select and use multiple traces in searching the optimal alignment.
Journal ArticleDOI

Vibronic coupling in indole: II. Investigation of the 1La–1Lb interaction using rotationally resolved electronic spectroscopy

TL;DR: High-resolution electronic spectra of indole (C(8)H(7)N) and their detailed analysis are reported and provide clear evidence for strong vibronic coupling of the two electronic states (1]L(b) and (1)L(a) and for the energetic location of the (1)-state more than 1000 cm(-1) above the ( 1)L (b) vibrationless state.
References
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Book

Genetic algorithms in search, optimization, and machine learning

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

Genetic algorithms in search, optimization and machine learning

TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book

Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book

Practical Genetic Algorithms

TL;DR: Introduction to Optimization The Binary genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.