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Hybrid Genetic-Fuzzy Algorithm for Variable Selection in Spectroscopy

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Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7895))

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Abstract

This paper presents a hybrid multi-objective genetic fuzzy algorithm for the variable-selection problem in spectroscopy. The problem formulation considers three fitness functions related to linear equations system stability. These fitness functions are models with fuzzy sets that evaluate the fitness solution for pick out the best to crossover. The population diversity is obtained applying the crowding distance method. The study shows that the selection by a fuzzy decision has better results than the selection by non-domination in problems where the fitness weighing is more proper than no-domination solutions.

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Author information

Authors and Affiliations

  1. Informatics Institute, Federal University of Goiás, Brazil

    Telma Woerle de Lima, Anderson da Silva Soares, Rogério Lopes Salvini & Gustavo Teodoro Laureano

  2. Computer Science Department, Pontifical University Catholic of Goiás, Brazil

    Clarimar José Coelho

Authors
  1. Telma Woerle de Lima
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  2. Anderson da Silva Soares
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  3. Clarimar José Coelho
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  4. Rogério Lopes Salvini
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  5. Gustavo Teodoro Laureano
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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de Lima, T.W., da Silva Soares, A., Coelho, C.J., Salvini, R.L., Laureano, G.T. (2013). Hybrid Genetic-Fuzzy Algorithm for Variable Selection in Spectroscopy. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_3

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  • DOI: https://doi.org/10.1007/978-3-642-38610-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38609-1

  • Online ISBN: 978-3-642-38610-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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