[1. Bansal, N., P. Kaur. A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm. – International Journal of Applied Metaheuristic Computing, Vol. 10, 2019, No 2, pp. 93-108.10.4018/IJAMC.2019040104]Search in Google Scholar
[2. Driankov, D., H. Hellendoorn, M. Reinfrank. An Introduction to Fuzzy Control. – Berlin, Heidelberg, Springer-Verlag, 1993.10.1007/978-3-662-11131-4]Search in Google Scholar
[3. Dumitrescu, D. Algoritmi Genetici si Strategii Evolutive – Aplicatii in Inteligenta Artificiala Siin Domenii Conexe. Cluj-Napoca, Editura Albastra, 2006.]Search in Google Scholar
[4. Elbaz, K., S.-L. Shen, A. Zhou, D.-J. Yuan, Y.-S. Xu. Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm. – Applied Sciences, Switzerland, Vol. 9, 2019, No 4, Art. No 780.10.3390/app9040780]Search in Google Scholar
[5. Espanola, J. L., A. A. Bandala, R. R. P. Vicerra, E. P. Dadios. Design of a Fuzzy-Genetic Controller for an Articulated Robot Gripper. – In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, October 2018, Art. No 8650431, 2019, pp. 1701-1706.10.1109/TENCON.2018.8650431]Search in Google Scholar
[6. Fidanova S., M. Paprzycki, O. Roeva. Hybrid GA-ACO Algorithm for a Model Parameters Identification Problem. – In: Proc. of 2014 Federated Conference on Computer Science and Information Systems, Vol. 2, 2014, pp. 413-420.10.15439/2014F373]Search in Google Scholar
[7. Galaviz-Aguilar, J. A., P. Roblin, J. R. Cárdenas-Valdez, E. Z-Flores, L. Trujillo, J. C. Nuñez-Pérez, O. Schütze. Comparison of a Genetic Programming Approach with ANFIS for Power Amplifier Behavioral Modeling and FPGA Implementation. – Soft Computing, Vol. 23, 2019, No 7, pp. 2463-2481.10.1007/s00500-017-2941-8]Search in Google Scholar
[8. Gámez, J. C., D. García, A. González, R. Pérez. An Approximation to Solve Regression Problems with a Genetic Fuzzy Rule Ordinal Algorithm. – Applied Soft Computing Journal, Vol. 78, 2019, pp. 13-28.10.1016/j.asoc.2019.02.012]Search in Google Scholar
[9. Ganduri, V. S. R. K., S. Ghosh, P. R. Patnaik. Mixing Control as a Device to Increase PHB Production in Batch Fermentations with Co-cultures of Lactobacillus Delbrueckii and Ralstoniaeutropha. – Process Biochemistry, Vol. 40, 2005, pp. 257-264.10.1016/j.procbio.2004.01.012]Search in Google Scholar
[10. Georgieva, P. Genetic Fuzzy System for Financial Management. – Cybernetics and Information Technologies, Vol. 18, 2018, No 2, pp. 20-35.10.2478/cait-2018-0025]Search in Google Scholar
[11. Gola, A., G. Kłosowski. Development of Computer-Controlled Material Handling Model by Means of Fuzzy Logic and Genetic Algorithms. – Neurocomputing, Vol. 338, 2019, pp. 381-392.10.1016/j.neucom.2018.05.125]Search in Google Scholar
[12. Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning. – Reading MA, Addison-Wesley Professional, 1989.]Search in Google Scholar
[13. Goyal, A., P. A. Sourav, P. Kalyanaraman. Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data. – Cybernetics and Information Technologies, Vol. 17, 2017, No 4, pp. 99-113.10.1515/cait-2017-0044]Search in Google Scholar
[14. Haupt, R. L., S. E. Haupt. Practical Genetic Algorithms. 2nd Edition. Wiley Interscience, 2004.10.1002/0471671746]Search in Google Scholar
[15. Holland, J. H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Cambridge, MA, USA, MIT Press, 1992.10.7551/mitpress/1090.001.0001]Search in Google Scholar
[16. Ignatova, M., V. Lyubenova. Adaptive Control of Fed-Batch Processes for Poly-β-Hydroxybutyrate Production by Mixed Culture, Compt. Rend. Acad. bulg. Sci., Vol. 60, 2007, No 5, pp. 517-524.]Search in Google Scholar
[17. Koprinkova-Hristova, P. Fuzzy Control Approach to Mixed Culture Cultivation for PHB Production Process. – In: Proc. of International Conference Automatics and Informatics’07, 3-6 October 2007, Sofia, Bulgaria, pp. III-65-III-68.]Search in Google Scholar
[18. Koprinkova-Hristova, P. ACD Approach to Optimal Control of Mixed Culture Cultivation for PHB Production Process – Sugar’s Time Profile Synthesis. – In: Proc. of IEEE Intelligent Systems IS’08, Methodology, Models, Applications and Emerging Technologies, 6-8 September 2008, Varna, Bulgaria, Vol. II, pp. 12-29-12-32.]Search in Google Scholar
[19. Koprinkova-Hristova, P. Knowledge-Based Approach to Control of Mixed Culture Cultivation for PHB Production Process. – Biotechnology and Biotechnological Equipment, Vol. 22, 2008, No 4, pp. 964-967.10.1080/13102818.2008.10817589]Search in Google Scholar
[20. Koprinkova-Hristova, P., G. Palm. Adaptive Critic Design with ESN Critic for Bioprocess Optimization. – Lecture Notes in Computer Science, Vol. 6353, 2010, pp. 438-447.10.1007/978-3-642-15822-3_54]Search in Google Scholar
[21. Koprinkova-Hristova, P., G. Kostov, S. Popova. Intelligent Optimization of a Mixed Culture Cultivation Process. – Int. J. Bioautomation, Vol. 19, 2015, No 1, pp. S113-S124.]Search in Google Scholar
[22. Koshiyama, A. S., R. Tanscheit, M. M. B. R. Vellasco. Automatic Synthesis of Fuzzy Systems: An Evolutionary Overview with a Genetic Programming Perspective. – Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 9, 2019, No 2, Art. Noe1251.10.1002/widm.1251]Search in Google Scholar
[23. Kuo, R. J., T. P. Q. Nguyen. Genetic Intuitionistic Weighted Fuzzy k-Modes Algorithm for Categorical Data. – Neurocomputing, Vol. 330, 2019, pp. 116-126.10.1016/j.neucom.2018.11.016]Search in Google Scholar
[24. Lin, H., R. Kong, J. Liu. Genetic Algorithm Based Clustering for Large-Scale Sensor Networks. – Cybernetics and Information Technologies, Vol. 15, 2015, No 6, Special Issue on Logistics, Informatics and Service Science, pp. 168-177.10.1515/cait-2015-0077]Search in Google Scholar
[25. Mitchell, M. An Introduction to Genetic Algorithms. Cambridge, MA, USA, MIT Press, 1998.10.7551/mitpress/3927.001.0001]Search in Google Scholar
[26. Patnaik, P. R. Neural Network Designs for Poly-b-Hydroxybutyrate Production Optimization under Simulated Industrial Conditions. – Biotechnology Letters, Vol. 27, 2005, pp. 409-415.10.1007/s10529-005-1775-015834806]Search in Google Scholar
[27. Pei, X., Y. Zhou, N. Wang. A Gaussian Process Regression Based on Variable Parameters Fuzzy Dominance Genetic Algorithm for B-TFPMM Torque Estimation. – Neurocomputing, Vol. 335, 2019, pp. 153-169.10.1016/j.neucom.2018.11.086]Search in Google Scholar
[28. Ponticelli, G. S., S. Guarino, V. Tagliaferri, O. Giannini. An Optimized Fuzzy-Genetic Algorithm for Metal Foam Manufacturing Process Control. – International Journal of Advanced Manufacturing Technology, Vol. 101, 2019, No 1-4, pp. 603-614.10.1007/s00170-018-2942-5]Search in Google Scholar
[29. Popova, S. On-line State and Parameters Estimation Based on Measurements of the Glucose in Mixed Culture System. – Biotechnology and Biotechnological Equipment, Vol. 20, 2006, No 3, pp. 208-214.10.1080/13102818.2006.10817402]Search in Google Scholar
[30. Popova, S. Adaptive Control for PHB Production. – Acta Universitasis Cibernesis, Series E, Food Technology, Vol. XI, 2007, pp. 17-25.]Search in Google Scholar
[31. Roeva, O. Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison. – Bioautomation, Vol. 3, 2005, pp. 19-28.]Search in Google Scholar
[32. Sarasvathi, V., N. C. S. N. Iyengar, S. Saha. QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks. – Cybernetics and Information Technologies, Vol. 15, 2015, No 1, pp. 69-83.10.1515/cait-2015-0007]Search in Google Scholar
[33. Seising, R., E. Trillas, J. Kacprzyk. Towards the Future of Fuzzy Logic. Switzerland, Springer, International Publishing, 2015.10.1007/978-3-319-18750-1]Search in Google Scholar
[34. Siddique, N. Intelligent Control. Springer, Switzerland, International Publishing, 2014.]Search in Google Scholar
[35. Sonika, J. A. Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR. – In: D. Mishra, X. S. Yang, A. Unal, Eds. Data Science and Big Data Analytics, Lecture Notes on Data Engineering and Communications Technologies, Vol. 16, 2019, pp. 11-27.10.1007/978-981-10-7641-1_2]Search in Google Scholar
[36. Tohyama, M., T. Patarinska, Z. Qiang, K. Shimizu. Modeling of the Mixed Culture and Periodic Control for PHB Production. – Biochemical Engineering Journal, Vol. 10, 2002, pp. 157-173.10.1016/S1369-703X(01)00184-X]Search in Google Scholar
[37. Yager, R. R., L. A. Zadeh. An Introduction to Fuzzy Logic Applications in Intelligent Systems. Kluwer Academic Publishers, 1992.10.1007/978-1-4615-3640-6]Search in Google Scholar
[38. Yang, H., D. Luo. Acyclic Real-Time Traffic Signal Control Based on a Genetic Algorithm. – Cybernetics and Information Technologies, Vol. 13, 2013, No 3, pp. 111-123.10.2478/cait-2013-0029]Search in Google Scholar
[39. Zadeh, L. Fuzzy Sets. – Information and Control, Vol. 8, 1965, pp. 338-353.10.1016/S0019-9958(65)90241-X]Search in Google Scholar