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On Different Algorithms for InterCriteria Relations Calculation

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Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 757))

Abstract

Contemporary InterCriteria analysis (ICrA) approach for searching of existing or unknown correlations between multiple objects against multiple criteria is applied here. Altogether five different algorithms for InterCriteria relations calculation have been examined to render the influence of the genetic algorithm parameters on the algorithm performance. Two cases, i.e. the model parameter identification of E. coli and S. cerevisiae fed-batch fermentation processes, are considered. In this investigation \(\mu \)-biased, Balanced, \(\nu \)-biased, Unbiased, as well as the newly elaborated and proposed here Weighted algorithm have been consequently applied and thoroughly examined. The obtained results for considered here two Case studies have been compared showing that the most reliable algorithm is the \(\mu \)-biased one.

All authors have contributed equally to this work.

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Acknowledgements

This work was supported by the Bulgarian National Scientific Fund under the grants DFNI-I-02-5 “InterCriteria Analysis—A New Approach to Decision Making” and DN-02/10 “New Instruments for Knowledge Discovery from Data, and their Modelling.”

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Correspondence to Olympia Roeva .

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Roeva, O., Vassilev, P., Ikonomov, N., Angelova, M., Su, J., Pencheva, T. (2019). On Different Algorithms for InterCriteria Relations Calculation. In: Hadjiski, M., Atanassov, K. (eds) Intuitionistic Fuzziness and Other Intelligent Theories and Their Applications. Studies in Computational Intelligence, vol 757. Springer, Cham. https://doi.org/10.1007/978-3-319-78931-6_10

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