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InterCriteria Analysis of Simple Genetic Algorithms Performance

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Advanced Computing in Industrial Mathematics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 681))

Abstract

Recently developed approach of InterCriteria Analysis is here applied aiming at an assessment of the performance of such a promising stochastic optimization technique as simple genetic algorithms. Considered algorithms, as representatives of the biologically-inspired ones, are chosen as an object of investigation since they are proven as quite successful in solving of many challenging problems in the field of complex dynamic systems optimization. In this investigation simple genetic algorithms are applied for the purposes of parameter identification of a fermentation process. Altogether six simple genetic algorithms are here considered, differ from each other in the execution order of main genetic operators, namely selection, crossover and mutation. The apparatuses of index matrices and intuitionistic fuzzy sets, underlying the InterCriteria Analysis, are implemented to assess the performance of simple genetic algorithms for the parameter identification of Saccharomyces cerevisiae fed-batch fermentation process. The obtained results after the InterCriteria Analysis application are thoroughly analysed towards the algorithms outcomes, such as convergence time and model accuracy.

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Acknowledgements

The work is supported by the Bulgarian National Scientific Fund under the grant DFNI-I-02-5 “InterCriteria Analysis—A New Approach to Decision Making”.

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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

    Tania Pencheva & Maria Angelova

Authors
  1. Tania Pencheva
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  2. Maria Angelova
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Correspondence to Tania Pencheva .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir Georgiev

  2. Faculty of Applied Mathematics and Informatics, Technical University of Sofia, Sofia, Bulgaria

    Michail Todorov

  3. Institute of Information and Communication Technologies, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Georgiev

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Pencheva, T., Angelova, M. (2017). InterCriteria Analysis of Simple Genetic Algorithms Performance. In: Georgiev, K., Todorov, M., Georgiev, I. (eds) Advanced Computing in Industrial Mathematics. Studies in Computational Intelligence, vol 681. Springer, Cham. https://doi.org/10.1007/978-3-319-49544-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-49544-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49543-9

  • Online ISBN: 978-3-319-49544-6

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