Issue:InterCriteria analysis of a cultivation process model based on the genetic algorithm population size influence

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Title of paper: InterCriteria analysis of a cultivation process model based on the genetic algorithm population size influence Author(s): Maria Angelova Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria maria.angelova@biomed.bas.bg Olympia Roeva Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria olympia@biomed.bas.bg Tania Pencheva Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. G. Bonchev Str., Sofia 1113, Bulgaria tania.pencheva@biomed.bas.bg Published in: "Notes on IFS", Volume 21, 2015, Number 4, pages 90–103 Download:  PDF (653  Kb, File info) Abstract: InterCriteria Analysis (ICA), based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets, is here applied. The main idea is ICA to be applied in order the relations of defined parameters in a non-linear model of a Saccharomyces cerevisiae fed-batch cultivation to be evaluated. Using genetic algorithms, series of identification procedures of considered model are performed. The influence of the genetic algorithm population size towards optimization technique outcomes, such as convergence time and objective function value, is investigated. Implementing ICA, relations between model parameters themselves, on the one hand, and genetic algorithms outcomes, on the other hand, are obtained. The thorough discussion of derived results are worth for, firstly, deeper cultivation process model understanding, and, further, a better performance of genetic algorithms to be achieved. Keywords: InterCriteria analysis, Genetic algorithms, Population size, Cultivation process, Saccharomyces cerevisiae. AMS Classification: 03E72. References: Angelova, M. (2014) Modified Genetic Algorithms and Intuitionistic Fuzzy Logic for Parameter Identification of Fed-batch Cultivation Model, PhD Thesis, Sofia. (in Bulgarian) Atanassov, K. (2012) On Intuitionistic Fuzzy Sets Theory, Springer, Berlin. Atanassov, K., Mavrov, D., & Atanassova, V. (2014) Intercriteria decision making: A new approach for multicriteria decision making, Issues in Intuitionistic Fuzzy Sets and Generalized Nets, 11, 1–8. Atanassov, K., Szmidt, E., & Kacprzyk, J. (2013) On intuitionistic fuzzy pairs, Notes on Intuitionistic Fuzzy Sets, 19(3), 1–13. Atanassov, K., Atanassova, V., & Gluhchev, G. (2015) InterCriteria Analysis: Ideas and problems, Notes on Intuitionistic Fuzzy Sets, 21(1), 81–88. Atanassov, K. (1987) Generalized index matrices, Comptes rendus de l’Academie Bulgare des Sciences, 40(11), 15–18. Atanassov, K. (2010) On index matrices, part 1: Standard cases, Advanced Studies in Contemporary Mathematics, 20(2), 291–302. Atanassov, K. (2010) On index matrices, part 2: Intuitionistic fuzzy case, Proceedings of the Jangjeon Mathematical Society, 13(2), 121–126. Diaz-Gomez, P. A., & Hougen, D. F. (2007) Initial population for genetic algorithms: A metric approach, Proc. of the International Conference on Genetic and Evolutionary Methods, GEM 2007, Las Vegas, USA, Eds. Arabnia, H. R., Yang, J. Y., & Yang, M. Q., 43–49. Goldberg, D. E. (2006) Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley Longman, London. Koumousis, V. K., & Katsaras, C. P. (2006) A sawtooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance, IEEE Transactions on Evolutionary Computation, 10(1), 19–28. Lobo, F. G., & Lima C. F. (2005) A review of adaptive population sizing schemes in genetic algorithms, Proc. of the Genetic and Evolutionary Computation Conference, 228–234. Obitko, M. (2005) Genetic Algorithms, http://www.obitko.com/tutorials/genetic-algorithms/ Piszcz, A., & Soule, T. (2006) Genetic programming: Optimal population sizes for varying complexity problems, Proc. of the Genetic and Evolutionary Computation Conference, 953– 954. Pencheva, T., Roeva, O., & Hristozov, I. (2006) Functional State Approach to Fermentation Processes Modelling, Prof. Marin Drinov Academic Publishing House, Sofia. Pencheva, T., Angelova, M., Atanassova, V., & Roeva, O. (2015) InterCriteria analysis of genetic algorithm parameters in parameter identification, Notes on Intuitionistic Fuzzy Sets, 21(2), 99–110. Roeva, O., Vassilev, P., Angelova, M., & Pencheva, T. (2015) Intercriteria analysis of parameters relations in fermentation processes models, Lecture Notes in Artificial Intelligence, 9330, 171–181. Roeva, O., Fidanova, S., & Paprzycki, M. (2015) Population size influence on the genetic and ant algorithms performance in case of cultivation process modeling, Recent Advances in Computational Optimization, Studies in Computational Intelligence, 580, 107–120. Citations: Issue:Knowledge discovery from data: InterCriteria Analysis of mutation rate influence ‎ (← links) The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.