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An Evolutionary Approach in Quantitative Spectroscopy

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Book cover Simulated Evolution and Learning (SEAL 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1585))

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Abstract

This paper describes investigations into using evolutionary search for quantitative spectroscopy. Given the spectrum (intensity × frequency) of a sample of material of interest, we would like to be able to infer the make-up of the material in terms of percentages by mass of its constituent compounds. The problem is usually tackled using regression methods. This approach can have various difficulties. We have cast the problem as an optimisation task. Using a hybrid of distributed genetic algorithm with a local search around the best individual of the population, very good results have been found, even with noise, for a number of different instances of the problem, with variations in the range between 6 and 16 constituent compounds. The stochastic optimisation approach shows great promise in overcoming many of the problems associated with the more standard regression techniques.

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References

  1. J. J. Baraga. PhD thesis, Massachusetts Institute of Technology, 1992.

    Google Scholar 

  2. R. Collins and D. Jefferson. Selection in massively parallel genetic algorithms. In R. K. Belew and L. B. Booker, editors, Proceedings of the Fourth Intl. Conf. on Genetic Algorithms, ICGA-91, pages 249–256. Morgan Kaufmann, 1991.

    Google Scholar 

  3. Galactic Industries Corporation. http://www.galactic.com/galactic/Science/algo.htm. Web Site, 1996.

  4. C. Kappler, T. Back, J. Heistermann, A.V. Velde, and M. Zamparelli. Refueling of a nuclear power plant: Comparison of a naive and a specialized mutation operator. In Proc. of PPSN IV, volume LNCS, 1141, pages 829–838. Springer, 1996.

    Google Scholar 

  5. J. Koza. Genetic Programming: On the programming of computers by means of natural selection. MIT Press, 1992.

    Google Scholar 

  6. E.H. Malinowski and D.G. Howery. Factor Analysis in Chemistry. John Wiley, 1980.

    Google Scholar 

  7. H. Mark. Analytical Chemistry, 58:2814, 1986.

    Article  Google Scholar 

  8. W. Press, W. Vetterling, S. Teukolsky, and B. Flannery. Numerical recipes in C (2/e). CUP, 1992.

    Google Scholar 

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© 1999 Springer-Verlag Berlin Heidelberg

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Husbands, P., de Oliveira, P.P.B. (1999). An Evolutionary Approach in Quantitative Spectroscopy. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_35

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  • DOI: https://doi.org/10.1007/3-540-48873-1_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65907-5

  • Online ISBN: 978-3-540-48873-6

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