A fast fine-grained genetic algorithm for spectrum fitting: An application to X-ray spectra

Often the result of the interaction of radiation with matter is represented by a spectrum. Regardless of the type of radiation used, a spectrum consists of peaks superimposed on a background. The important information is contained in the peak, so the derivation of the peaks parameters is fundamental. However, in most cases the fitting of the spectrum is an ill-posed problem, because of the presence of superimposed peaks. In this case the algorithms reported in the literature sometimes fail. For these reasons a genetic algorithm approach has been developed and is described here. Its novelty for this kind of application is represented by the use of a fine-grained strategy that makes it considerably faster than other implementations of this class of algorithms. Some results of its application to X-ray spectra are presented and discussed.

[1]  J. H. Hubbell,et al.  XCOM : Photon Cross Sections Database , 2005 .

[2]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[3]  X-ray tomography as an estimation technique for wood consolidation by frontal polymerization , 2007 .

[4]  S. Ridolfi,et al.  Pigment layers and precious metal sheets by energy-dispersive x-ray fluorescence analysis , 2008 .

[5]  R Cesareo,et al.  Pre-Columbian alloys from the royal tombs of Sipán; energy dispersive X-ray fluorescence analysis with a portable equipment. , 2010, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[6]  Enrique Alba,et al.  Comparing Synchronous and Asynchronous Cellular Genetic Algorithms , 2002, PPSN.

[7]  S. Chervenkov,et al.  Fragmentation and conformation study of ephedrine by low- and high-resolution mass selective UV spectroscopy. , 2004, The Journal of chemical physics.

[8]  T. Chakraborty,et al.  Specific and nonspecific interactions in a molecule with flexible side chain: 2-phenylethanol and its 1:1 complex with argon studied by high-resolution UV spectroscopy. , 2006, The Journal of chemical physics.

[9]  Enrique Alba,et al.  Analyzing synchronous and asynchronous parallel distributed genetic algorithms , 2001, Future Gener. Comput. Syst..

[10]  Dayong Jin,et al.  Long-lived visible luminescence of UV LEDs and impact on LED excited time-resolved fluorescence applications , 2006 .

[11]  Bruno Golosio,et al.  The xraylib library for X-ray-matter interactions. Recent developments , 2011 .

[12]  B. Schönfisch,et al.  Synchronous and asynchronous updating in cellular automata. , 1999, Bio Systems.

[13]  V. A. Solé,et al.  A multiplatform code for the analysis of energy-dispersive X-ray fluorescence spectra , 2007 .

[14]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[15]  A. Generosi,et al.  Energy dispersive x-ray reflectometry as a unique laboratory tool for investigating morphological properties of layered systems and devices , 2006 .

[16]  Antonio Brunetti,et al.  An X-ray Diffraction (XRD) and X-ray Fluorescence (XRF) investigation in human and animal fossil bones from Holocene to Middle Triassic , 2009 .

[17]  Ron Wehrens,et al.  Direct determination of molecular constants from rovibronic spectra with genetic algorithms , 2000 .

[18]  S. Steenstrup,et al.  A simple procedure for fitting a backgound to a certain class of measured spectra , 1981 .

[19]  Caroline Mellot-Draznieks,et al.  Role of computer simulations in structure prediction and structure determination: from molecular compounds to hybrid frameworks , 2007 .

[20]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[21]  Michael Schmitt,et al.  Application of genetic algorithms in automated assignments of high-resolution spectra , 2006 .

[22]  李幼升,et al.  Ph , 1989 .

[23]  A Brunetti,et al.  Selective energy dispersive diffraction peak fitting by using genetic algorithm. , 2010, Journal of X-ray science and technology.

[24]  A. Brunetti,et al.  Angular calibration in energy dispersive X-Ray diffraction by using genetic algorithms. , 2009, Journal of X-ray science and technology.

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[26]  S. N. Sivanandam,et al.  Introduction to genetic algorithms , 2007 .

[27]  M. Chukalina,et al.  Internal elemental microanalysis combining x-ray fluorescence, Compton and transmission tomography , 2003 .

[28]  Roy L. Johnston,et al.  Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data , 2003, J. Comput. Chem..

[29]  W. David.  Powder diffraction peak shapes. Parameterization of the pseudo-Voigt as a Voigt function , 1986 .

[30]  Alexandre Simionovici,et al.  A library for X-ray-matter interaction cross sections for X-ray fluorescence applications , 2004 .

[31]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[32]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[33]  Antonio Brunetti,et al.  A Fast and precise genetic algorithm for a non-linear fitting problem , 2000 .

[34]  Roy L. Johnston,et al.  Applications of Evolutionary Computation in Structure Determination from Diffraction Data , 2004 .

[35]  D. R. Cousens,et al.  SNIP, A STATISTICS-SENSITIVE BACKGROUND TREATMENT FOR THE QUANTITATIVE-ANALYSIS OF PIXE SPECTRA IN GEOSCIENCE APPLICATIONS , 1988 .