Genetic Algorithms vs. Knowledge-Based Control of PHB Production

Abstract The paper proposes an approach using Genetic Algorithm (GA) for development of optimal time profiles of key control variable of Poly-HydroxyButyrate (PHB) production process. Previous work on modeling and simulation of PHB process showed that it is a highly nonlinear process that needs special controllers based on human experience, as such fuzzy logic controller proved to be a good choice. Fuzzy controllers are not totally replaced, due to the specific process knowledge that they contain. The achieved results are compared with previously proposed knowledge-based approach to the same optimal control task.

[1]  Na Wang,et al.  A Gaussian process regression based on variable parameters fuzzy dominance genetic algorithm for B-TFPMM torque estimation , 2019, Neurocomputing.

[2]  Stefano Guarino,et al.  An optimized fuzzy-genetic algorithm for metal foam manufacturing process control , 2018, The International Journal of Advanced Manufacturing Technology.

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Arkadiusz Gola,et al.  Development of computer-controlled material handling model by means of fuzzy logic and genetic algorithms , 2019, Neurocomputing.

[5]  Annan Zhou,et al.  Optimization of EPB Shield Performance with Adaptive Neuro-Fuzzy Inference System and Genetic Algorithm , 2019, Applied Sciences.

[6]  Silviya Popova.  ADAPTIVE CONTROL FOR PHB PRODUCTION , 2007 .

[7]  Janusz Kacprzyk,et al.  Towards the Future of Fuzzy Logic , 2015, Towards the Future of Fuzzy Logic.

[8]  David García,et al.  An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm , 2019, Appl. Soft Comput..

[9]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[10]  Ricardo Tanscheit,et al.  Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective , 2019, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[11]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[12]  Kazuyuki Shimizu,et al.  Modeling of the mixed culture and periodic control for PHB production , 2002 .

[13]  P. Patnaik.  Neural network designs for poly- β-hydroxybutyrate production optimization under simulated industrial conditions , 2005, Biotechnology Letters.

[14]  Dayong Luo,et al.  Acyclic Real-Time Traffic Signal Control Based on a Genetic Algorithm , 2013 .

[15]  Akarsh Goyal,et al.  Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data , 2017 .

[16]  P. Koprinkova-Hristova.  ACD approach to optimal control of mixed culture cultivation for PHB production process — sugar’s time profile synthesis , 2008, 2008 4th International IEEE Conference Intelligent Systems.

[17]  Argel A. Bandala,et al.  Design of a Fuzzy-Genetic Controller for an Articulated Robot Gripper , 2018, TENCON 2018 - 2018 IEEE Region 10 Conference.

[18]  Snehanshu Saha,et al.  QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks , 2015, ArXiv.

[19]  Hai Lin,et al.  Genetic Algorithm Based Clustering for Large-Scale Sensor Networks , 2015 .

[20]  Petia Koprinkova-Hristova,et al.  Intelligent Optimization of a Mixed Culture Cultivation Process , 2015 .

[21]  Olympia Roeva,et al.  Hybrid GA-ACO Algorithm for a model parameters identification problem , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[22]  Sonika,et al.  Genetic Algorithm Approach for Optimization of Biomass Estimation at LiDAR , 2018, WIR.

[23]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[24]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[25]  Penka Georgieva.  Genetic Fuzzy System for Financial Management , 2018 .

[26]  Barbara Hayes-Roth,et al.  Intelligent Control , 1994, Artif. Intell..

[27]  R. J. Kuo,et al.  Genetic intuitionistic weighted fuzzy k-modes algorithm for categorical data , 2019, Neurocomputing.

[28]  Olympia Roeva.  Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison , 2005 .

[29]  L. Zadeh,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .

[30]  Parvinder Kaur,et al.  A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm , 2019, Int. J. Appl. Metaheuristic Comput..

[31]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[32]  Patrick Roblin,et al.  Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation , 2017, Soft Computing.

[33]  S. Popova,et al.  On—Line State and Parameters Estimation Based Measurements of the Glucose in Mixed Culture System , 2006 .

[34]  Günther Palm,et al.  Adaptive Critic Design with ESN Critic for Bioprocess Optimization , 2010, ICANN.

[35]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .