Abstract
In this paper we apply InterCriteria Analysis (ICrA) approach based on the apparatus of Index Matrices and Intuitionistic Fuzzy Sets. The main idea is to use ICrA to establish the existing relations and dependencies of defined parameters in a non-linear model of an E. coli fed-batch cultivation process. We perform a series of model identification procedures applying Genetic Algorithms (GAs). We proposed a schema of ICrA of ICrA results to examine the obtained model identification results. The discussion about existing relations and dependencies is performed according to criteria defined in terms of ICrA. We consider as ICrA criteria model parameters and GAs outcomes on the one hand, and 14 differently tuned GAs on the other. Based on the results, we observe the mutual relations between model parameters and GAs outcomes, such as computation time and objective function value. Moreover, some conclusions about the preferred tuned GAs for the considered model parameter identification in terms of achieved accuracy for given computation time are presented.
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Acknowledgments
The work presented here is partially supported by the Bulgarian National Scientific Fund under Grant DFNI-I02/5 and by the Polish-Bulgarian collaborative Grant “Parallel and Distributed Computing Practices”.
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Roeva, O., Vassilev, P., Fidanova, S., Paprzycki, M. (2016). InterCriteria Analysis of Genetic Algorithms Performance. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-319-40132-4_14
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DOI: https://doi.org/10.1007/978-3-319-40132-4_14
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