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Genetic Operators Significance Assessment in Simple Genetic Algorithm

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Large-Scale Scientific Computing (LSSC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8353))

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Abstract

Genetic algorithms, proved as successful alternative to conventional optimization methods for the purposes of parameter identification of fermentation process models, search for a global optimal solution via three main genetic operators, namely selection, crossover, and mutation. In order to determine their importance for finding the solution, a procedure for significance assessment of genetic algorithms operators has been developed. The workability of newly elaborated procedure has been tested when simple genetic algorithm is applied to parameter identification of S. cerevisiae fed-batch cultivation. According to obtained results the most significant genetic operator has been distinguished and its influence for finding the global optimal solution has been evaluated.

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Acknowledgements

This work is partially supported by National Science Fund of Bulgaria, grants DID 02-29 and DMU 03-38.

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., 1113, Sofia, Bulgaria

    Maria Angelova & Tania Pencheva

Authors
  1. Maria Angelova
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  2. Tania Pencheva
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Corresponding author

Correspondence to Maria Angelova .

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Editors and Affiliations

  1. Inst. of Info. & Comm. Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

  3. Dept. of Informatics and Mathematical Modell, Technical University of Denmark, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Angelova, M., Pencheva, T. (2014). Genetic Operators Significance Assessment in Simple Genetic Algorithm. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_24

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  • DOI: https://doi.org/10.1007/978-3-662-43880-0_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43879-4

  • Online ISBN: 978-3-662-43880-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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