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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© 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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