Journal articles: 'Genetic algorithms and fuzzy logic'

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TAKAGI, Hideyuki. "Genetic Algorithms and Fuzzy Logic." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 4 (1998): 602–12. http://dx.doi.org/10.3156/jfuzzy.10.4_22.

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Herrera, F., M. Lozano, and J. L. Verdegay. "Tuning fuzzy logic controllers by genetic algorithms." International Journal of Approximate Reasoning 12, no. 3-4 (April 1995): 299–315. http://dx.doi.org/10.1016/0888-613x(94)00033-y.

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RenHou, Li, and Zhang Yi. "Fuzzy logic controller based on genetic algorithms." Fuzzy Sets and Systems 83, no. 1 (October 1996): 1–10. http://dx.doi.org/10.1016/0165-0114(95)00337-1.

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Shill, Pintu Chandra, Animesh Kumar Paul, and Kazuyuki Murase. "Adaptive Fuzzy Logic Controllers Using Hybrid Genetic Algorithms." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 01 (February 2019): 41–71. http://dx.doi.org/10.1142/s021848851950003x.

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In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.

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Saini, J. S., M. Gopal, and A. P. Mittal. "Evolving Optimal Fuzzy Logic Controllers by Genetic Algorithms." IETE Journal of Research 50, no. 3 (May 2004): 179–90. http://dx.doi.org/10.1080/03772063.2004.11665504.

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Noshadi, Tayebe, Marzieh Dadvar, Nastaran Mirza, and Shima Shamseddini. "Adjust genetic algorithm parameter by fuzzy system." Ciência e Natura 37 (December 19, 2015): 190. http://dx.doi.org/10.5902/2179460x20771.

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Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

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Drir, Nadia, Linda Barazane, and Malik Loudini. "Optimizing the operation of a photovoltaic generator by a genetically tuned fuzzy controller." Archives of Control Sciences 23, no. 2 (June 1, 2013): 145–67. http://dx.doi.org/10.2478/acsc-2013-0009.

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This paper presents design and application of advanced control scheme which integrates fuzzy logic concepts and genetic algorithms to track the maximum power point in photovoltaic system. The parameters of adopted fuzzy logic controller are optimized using genetic algorithm with innovative tuning procedures. The synthesized genetic algorithm which optimizes fuzzy logic controller is implemented and tested to achieve a precise control of the maximum power point response of the photovoltaic generator. The performance of the adopted control strategy is examined through a series of simulation experiments which prove good tracking properties and fast response to changes of different meteorological conditions such as isolation or temperature.

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Wu, Xiao Qin. "The Application Research on Fuzzy Theory and Genetic Algorithm." Applied Mechanics and Materials 241-244 (December 2012): 1768–71. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1768.

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Fuzzy theory is one of the newly adduced self-adaptive strategies,which is applied to dynamically adjust the parameters of genetic algorithms for the purpose of enhancing the performance.In this paper, the financial time series analysis and forecasting as the main case study to the theory of soft computing technology framework that focuses on the fuzzy logic genetic algorithms(FGA) as a method of integration. the financial time series forecasting model based on fuzzy theory and genetic algorithms was built. the ShangZheng index cards as an example. The experimental results show that FGA perform s much better than BP neural network，not only in the precision.but also in the searching speed.The hybrid algorithm has a strong feasibility and superiority.

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Al-Tikriti, Munther N., and Rokaia Sh Al-Joubori. "Multi-Population Genetic Algorithms for Tuning Fuzzy Logic Controller." i-manager's Journal on Software Engineering 2, no. 2 (December 15, 2007): 56–63. http://dx.doi.org/10.26634/jse.2.2.593.

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Lau, H., T. M. Chan, and W. T. Tsui. "Item-Location Assignment Using Fuzzy Logic Guided Genetic Algorithms." IEEE Transactions on Evolutionary Computation 12, no. 6 (December 2008): 765–80. http://dx.doi.org/10.1109/tevc.2008.924426.

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Saggiani, G. M., G. Caligiana, and F. Persiani. "Multiobjective wing design using genetic algorithms and fuzzy logic." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 218, no. 2 (February 2004): 133–45. http://dx.doi.org/10.1243/0954410041321961.

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Arslan, Ahmet, and Mehmet Kaya. "Determination of fuzzy logic membership functions using genetic algorithms." Fuzzy Sets and Systems 118, no. 2 (March 2001): 297–306. http://dx.doi.org/10.1016/s0165-0114(99)00065-2.

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Tedford, J. D., and C. Lowe. "Production scheduling using adaptable fuzzy logic with genetic algorithms." International Journal of Production Research 41, no. 12 (January 2003): 2681–97. http://dx.doi.org/10.1080/0020754031000090621.

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Song, Y. H., G. S. Wang, P. Y. Wang, and A. T. Johns. "Environmental/economic dispatch using fuzzy logic controlled genetic algorithms." IEE Proceedings - Generation, Transmission and Distribution 144, no. 4 (1997): 377. http://dx.doi.org/10.1049/ip-gtd:19971100.

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Phillips, Chad, Charles L. Karr, and Greg Walker. "Helicopter flight control with fuzzy logic and genetic algorithms." Engineering Applications of Artificial Intelligence 9, no. 2 (April 1996): 175–84. http://dx.doi.org/10.1016/0952-1976(95)00008-9.

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Changizi, Nemat, Mahbubeh Moghadas, Mohamad Reza Dastranj, and Mohsen Farshad. "Design a Fuzzy Logic Based Speed Controller for DC Motor with Genetic Algorithm Optimization." Applied Mechanics and Materials 110-116 (October 2011): 2324–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2324.

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In this paper, an intelligent speed controller for DC motor is designed by combination of the fuzzy logic and genetic algorithms. First, the speed controller is designed according to fuzzy rules such that the DC drive is fundamentally robust. Then, to improve the DC drive performance, parameters of the fuzzy speed controller are optimized by using the genetic algorithm. Simulation works in MATLAB environment demonstrate that the genetic optimized fuzzy speed controller became very strong, gives very good results and possesses good robustness.

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Ishibuchi, Hisao, Naohisa Yamamoto, Tadahiko Murata, and Hideo Tanaka. "Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems." Fuzzy Sets and Systems 67, no. 1 (October 1994): 81–100. http://dx.doi.org/10.1016/0165-0114(94)90210-0.

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Last, Mark, and Shay Eyal. "A fuzzy-based lifetime extension of genetic algorithms." Fuzzy Sets and Systems 149, no. 1 (January 2005): 131–47. http://dx.doi.org/10.1016/j.fss.2004.07.011.

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Pham, D. T., and D. Karaboga. "Genetic algorithms with variable mutation rates: Application to fuzzy logic controller design." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 211, no. 2 (March 1, 1997): 157–67. http://dx.doi.org/10.1243/0959651971539975.

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Three variable mutation rate strategies for improving the performance of genetic algorithms (GAs) are described. The problem of optimizing fuzzy logic controllers is used to evaluate a GA adopting these strategies against a GA employing a static mutation regime. Simulation results for a second-order time-delayed system controlled by fuzzy logic controllers (FLCs) obtained using the different GAs are presented.

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Chen, D. G., N. B. Hargreaves, D. M. Ware, and Y. Liu. "A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships." Canadian Journal of Fisheries and Aquatic Sciences 57, no. 9 (September 1, 2000): 1878–87. http://dx.doi.org/10.1139/f00-141.

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A new fuzzy logic model with a genetic algorithm is developed that overcomes some of the inherent uncertainties in the fish stock-recruitment process. This model is applied to stock-recruitment relationships for the Southeast Alaska pink salmon (Oncorhynchus gorbuscha) and the West Coast Vancouver Island Pacific herring (Clupea pallasi) stocks. In both examples, the annual mean sea surface temperature is used as an environmental intervention in the model. The fuzzy logic model provides the functional relationship between the number of fish spawners and the sea surface temperature that is used to reconstruct the historical fish recruitment time series and also to predict the number of fish that will recruit in the future. Globally optimized genetic learning algorithms are used to find the optimal values of the parameters for the fuzzy logic model. The results from this fuzzy logic model are compared with results from both a traditional Ricker stock-recruitment model and a recent artificial neural network model. These comparisons demonstrate the superior capability of the fuzzy logic model for addressing problems of uncertainty and vagueness in both the data and the stock-recruitment relationship. The fuzzy logic model approach is recommended as a useful addition to the analytical tools currently available for fish stock assessment and management.

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Pencheva, Tania, Maria Angelova, Evdokia Sotirova, and Krassimir Atanassov. "How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic." Mathematics 9, no. 18 (September 7, 2021): 2189. http://dx.doi.org/10.3390/math9182189.

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Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

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An, Young-Hwa, and Key-Ho Kwon. "Parallel Genetic Algorithm using Fuzzy Logic." KIPS Transactions:PartA 13A, no. 1 (February 1, 2006): 53–56. http://dx.doi.org/10.3745/kipsta.2006.13a.1.053.

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Tanaka, M., J. Ye, and T. Tanino. "Identification of Nonlinear Systems using Fuzzy Logic and Genetic Algorithms." IFAC Proceedings Volumes 27, no. 8 (July 1994): 265–70. http://dx.doi.org/10.1016/s1474-6670(17)47726-3.

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Shapiro, Arnold F. "The merging of neural networks, fuzzy logic, and genetic algorithms." Insurance: Mathematics and Economics 31, no. 1 (August 2002): 115–31. http://dx.doi.org/10.1016/s0167-6687(02)00124-5.

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Alkhawlani, Mohammed, and Aladdin Ayesh. "Access Network Selection Based on Fuzzy Logic and Genetic Algorithms." Advances in Artificial Intelligence 2008 (May 25, 2008): 1–12. http://dx.doi.org/10.1155/2008/793058.

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In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.

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Jinwoo Kim and B. P. Zeigler. "Hierarchical distributed genetic algorithms: a fuzzy logic controller design application." IEEE Expert 11, no. 3 (June 1996): 76–84. http://dx.doi.org/10.1109/64.506756.

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Arotaritei, Dragos. "Genetic Algorithm for Fuzzy Neural Networks using Locally Crossover." International Journal of Computers Communications & Control 6, no. 1 (March 1, 2011): 8. http://dx.doi.org/10.15837/ijccc.2011.1.2196.

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Fuzzy feed-forward (FFNR) and fuzzy recurrent networks (FRNN) proved to be solutions for "real world problems". In the most cases, the learning algorithms are based on gradient techniques adapted for fuzzy logic with heuristic rules in the case of fuzzy numbers. In this paper we propose a learning mechanism based on genetic algorithms (GA) with locally crossover that can be applied to various topologies of fuzzy neural networks with fuzzy numbers. The mechanism is applied to FFNR and FRNN with L-R fuzzy numbers as inputs, outputs and weights and fuzzy arithmetic as forward signal propagation. The α-cuts and fuzzy biases are also taken into account. The effectiveness of the proposed method is proven in two applications: the mapping a vector of triangular fuzzy numbers into another vector of triangular fuzzy numbers for FFNR and the dynamic capture of fuzzy sinusoidal oscillations for FRNN.

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Olteanu, Marius, Nicolae Paraschiv, and Petia Koprinkova-Hristova. "Genetic Algorithms vs. Knowledge-Based Control of PHB Production." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 104–16. http://dx.doi.org/10.2478/cait-2019-0018.

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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.

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Guo, Li-Xin, and Dinh-Nam Dao. "A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system." Journal of Vibration and Control 26, no. 13-14 (December 30, 2019): 1187–98. http://dx.doi.org/10.1177/1077546319890188.

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This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

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Hyun-Joon, Cho, Cho Kwang-Bo, and Wang Bo-Hyeun. "Fuzzy-PID hybrid control: Automatic rule generation using genetic algorithms." Fuzzy Sets and Systems 92, no. 3 (December 1997): 305–16. http://dx.doi.org/10.1016/s0165-0114(96)00175-3.

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Olivas, Frumen, Ivan Amaya, José Carlos Ortiz-Bayliss, Santiago E. Conant-Pablos, and Hugo Terashima-Marín. "Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic." Computational Intelligence and Neuroscience 2021 (January 25, 2021): 1–17. http://dx.doi.org/10.1155/2021/8834324.

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Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.

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Galantucci, L. M., G. Percoco, and R. Spina. "Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms." International Journal of Advanced Robotic Systems 1, no. 2 (June 2004): 7. http://dx.doi.org/10.5772/5622.

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Foroutan, E., M. R. Delavar, and B. N. Araabi. "INTEGRATION OF GENETIC ALGORITHMS AND FUZZY LOGIC FOR URBAN GROWTH MODELING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-2 (July 12, 2012): 69–74. http://dx.doi.org/10.5194/isprsannals-i-2-69-2012.

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CHATURVEDI, RUCHI, BABITA PATHIK, and SHIV KUMAR. "Intrusion Detection Using Data Mining Along Fuzzy Logic & Genetic Algorithms." JOURNAL OF COMPUTER AND INFORMATION TECHNOLOGY 09, no. 01 (February 2, 2018): 9–13. http://dx.doi.org/10.22147/jucit/090102.

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Li, H., P. T. Chan, A. B. Rad, and Y. K. Wong. "Optimization of Scaling Factors of Fuzzy Logic Controllers by Genetic Algorithms." IFAC Proceedings Volumes 30, no. 25 (September 1997): 347–52. http://dx.doi.org/10.1016/s1474-6670(17)41347-4.

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Chin, T. C., and X. M. Qi. "Genetic algorithms for learning the rule base of fuzzy logic controller." Fuzzy Sets and Systems 97, no. 1 (July 1998): 1–7. http://dx.doi.org/10.1016/s0165-0114(96)00354-5.

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Chih-Knan Chiang, Hung-Yuan Chung, and Jin-Jye Lin. "A self-learning fuzzy logic controller using genetic algorithms with reinforcements." IEEE Transactions on Fuzzy Systems 5, no. 3 (1997): 460–67. http://dx.doi.org/10.1109/91.618280.

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Ramalho, M. F., and E. M. Scharf. "Fuzzy logic tool and genetic algorithms for CAC in ATM networks." Electronics Letters 32, no. 11 (1996): 973. http://dx.doi.org/10.1049/el:19960672.

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Shill, Pintu Chandra, M. A. H. Akhand, MD Asaduzzaman, and Kazuyuki Murase. "Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms." International Journal of Information Technology & Decision Making 14, no. 05 (September 2015): 1063–92. http://dx.doi.org/10.1142/s0219622015500273.

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In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the first phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the conflicting rules during the learning process. The first and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods.

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Odeh, S. M., A. M. Mora, M. N. Moreno, and J. J. Merelo. "A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System." Advances in Fuzzy Systems 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/378156.

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This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

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Mishra, Shashwati, and Mrutyunjaya Panda. "Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions." International Journal of Fuzzy System Applications 8, no. 4 (October 2019): 39–59. http://dx.doi.org/10.4018/ijfsa.2019100103.

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Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

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Hu, Huangshui, Tingting Wang, Siyuan Zhao, and Chuhang Wang. "Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller." Advances in Mechanical Engineering 11, no. 11 (November 2019): 168781401989019. http://dx.doi.org/10.1177/1687814019890199.

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In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

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Chengqi Zhang*, Ling Guan**, and Zheru Chi. "Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 439–40. http://dx.doi.org/10.20965/jaciii.1999.p0439.

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Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range of applications. This Special Issue presents seven papers on learning in intelligent algorithms and systems design from researchers in Japan, China, Australia, and the U.S. Neural Networks: Emulating low-level human intelligent processing, or neuronal processing, gave birth of artificial neural networks more than five decades ago. It was hoped that devices based on biological neural networks would possess characteristics of the human brain. Neural networks have reattracted researchers' attention since the late 1980s when back-propagation algorithms were used to train multilayer feed-forward neural networks. In the last decades, we have seen promising progress in this research field yield many new models, learning algorithms, and real-world applications, evidenced by the publication of new journals in this field. Fuzzy Logic: Since L. A. Zadeh introduced fuzzy set theory in 1965, fuzzy logic has increasingly become the focus of many researchers and engineers opening up new research and problem solving. Fuzzy set theory has been favorably applied to control system design. In the last few years, fuzzy model applications have bloomed in image processing and pattern recognition. Evolutionary Algorithms: Evolutionary optimization algorithms have been studied over three decades, emulating natural evolutionary search and selection so powerful in global optimization. The study of evolutionary algorithms includes evolutionary programming (EP), evolutionary strategies (ESs), genetic algorithms (GAs), and genetic programming (GP). In the last few years, we have also seen multiple computational algorithms combined to maximize system performance, such as neurofuzzy networks, fuzzy neural networks, fuzzy logic and genetic optimization, neural networks, and evolutionary algorithms. This Special Issue also includes papers that introduce combined techniques. Wang et al present an improved fuzzy algorithm for enhanced eyeground images. Examination of the eyeground image is effective in diagnosing glaucoma and diabetes. Conventional eyeground image quality is usually too poor for doctors to obtain useful information, so enhancement is required to eliminate this. Due to details and uncertainties in eyeground images, conventional enhancement such as histogram equalization, edge enhancement, and high-pass filters fail to achieve good results. Fuzzy enhancement enhances images in three steps: (1) transferring an image from the spatial domain to the fuzzy domain; (2) conducting enhancement in the fuzzy domain; and (3) returning the image from the fuzzy domain to the spatial domain. The paper detailing this proposes improved mapping and fast implementation. Mohammadian presents a method for designing self-learning hierarchical fuzzy logic control systems based on the integration of evolutionary algorithms and fuzzy logic. The purpose of such an approach is to provide an integrated knowledge base for intelligent control and collision avoidance in a multirobot system. Evolutionary algorithms are used as in adaptation for learning fuzzy knowledge bases of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems. Fuzzy integral has been found useful in data fusion. Pham and Wagner present an approach based on the fuzzy integral and GAs to combine likelihood values of cohort speakers. The fuzzy integral nonlinearly fuses similarity measures of an utterance assigned to cohort speakers. In their approach, Gas find optimal fuzzy densities required for fuzzy fusion. Experiments using commercial speech corpus T146 show their approach achieves more favorable performance than conventional normalization. Evolution reflects the behavior of a society. Puppala and Sen present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. Agent behavior evolves via GAs, where one genetic algorithm population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and best strategy pairs are stored together in shared memory. The approach is evaluated using asymmetric room painting and results demonstrate the superiority of shared memory over random pairing in consistently generating optimal behavior patterns. Object representation and template optimization are two main factors affecting object recognition performance. Lu et al present an evolutionary algorithm for optimizing handwritten numeral templates represented by rational B-spline surfaces of character foreground-background-distance distribution maps. Initial templates are extracted from training a feed-forward neural network instead of using arbitrarily chosen patterns to reduce iterations required in evolutionary optimization. To further reduce computational complexity, a fast search is used in selection. Using 1,000 optimized numeral templates, the classifier achieves a classification rate of 96.4% while rejecting 90.7% of nonnumeral patterns when tested on NIST Special Database 3. Determining an appropriate number of clusters is difficult yet important. Li et al based their approach based on rival penalized competitive learning (RPCL), addressing problems of overlapped clusters and dependent components of input vectors by incorporating full covariance matrices into the original RPCL algorithm. The resulting learning algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is applied to determine the number of clusters in a Gaussian mixture distribution and to optimize the architecture of elliptical function networks for speaker verification and for vowel classification. Another important issue on learning is Kurihara and Sugawara's adaptive reinforcement learning algorithm integrating exploitation- and exploration-oriented learning. This algorithm is more robust in dynamically changing, large-scale environments, providing better performance than either exploitation- learning or exploration-oriented learning, making it is well suited for autonomous systems. In closing we would like to thank the authors who have submitted papers to this Special Issue and express our appreciation to the referees for their excellent work in reading papers under a tight schedule.

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Sakawa, Masatoshi, and Ichiro Nishizaki. "Interactive fuzzy programming for two-level nonconvex programming problems with fuzzy parameters through genetic algorithms." Fuzzy Sets and Systems 127, no. 2 (April 2002): 185–97. http://dx.doi.org/10.1016/s0165-0114(01)00134-8.

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Shabanov, K. B., and V. V. Alekseev. "Application of Intellectual Methods of Data Analysis to Improve the Quality of Decision Making in Management of Resources for the Information Media System." Vestnik Tambovskogo gosudarstvennogo tehnicheskogo universiteta 27, no. 1 (2021): 014–19. http://dx.doi.org/10.17277/vestnik.2021.01.pp.014-019.

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In connection with the relevance of data analysis automation, basic data mining methods are considered, such as neural networks, genetic algorithms, and fuzzy logic methods. The essence of these methods and their practical applicability are shown, in particular, methods of fuzzy logic for the problem of improving the quality of decision-making when managing the resources of the information media system.

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Sarimveis, Haralambos, and George Bafas. "Fuzzy model predictive control of non-linear processes using genetic algorithms." Fuzzy Sets and Systems 139, no. 1 (October 2003): 59–80. http://dx.doi.org/10.1016/s0165-0114(02)00506-7.

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Jain, Rachna, and Arun Sharma. "ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS." Journal of Engineering Research [TJER] 16, no. 1 (May 9, 2019): 11. http://dx.doi.org/10.24200/tjer.vol16iss1pp11-17.

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The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms. A genetic algorithm has been explored for predicting software reliability. One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions.

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Varnamkhasti, M. Jalali. "A genetic algorithm rooted in integer encoding and fuzzy controller." IAES International Journal of Robotics and Automation (IJRA) 8, no. 2 (June 1, 2019): 113. http://dx.doi.org/10.11591/ijra.v8i2.pp113-124.

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The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.

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Mohammadian, Masoud. "Modelling, Control and Prediction using Hierarchical Fuzzy Logic Systems." International Journal of Fuzzy System Applications 6, no. 3 (July 2017): 105–23. http://dx.doi.org/10.4018/ijfsa.2017070105.

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Hierarchical fuzzy logic systems are increasingly applied to solve complex problems. There is a need for a structured and methodological approach for the design and development of hierarchical fuzzy logic systems. In this paper a review of a method developed by the author for design and development of hierarchical fuzzy logic systems is considered. The proposed method is based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for modelling, control and prediction. Issues related to the design and construction of hierarchical fuzzy logic systems using several applications are considered and methods for the decomposition of complex systems into hierarchical fuzzy logic systems are proposed. Decomposition and conversion of systems into hierarchical fuzzy logic systems reduces the number of fuzzy rules and improves the learning speed for such systems. Application areas considered are: the prediction of interest rate and hierarchical robotic control. The aim of this manuscript is to review and highlight the research work completed in the area of hierarchical fuzzy logic system by the author. The paper can benefit researchers interested in the application of hierarchical fuzzy logic systems in modelling, control and prediction.

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Georgescu, Vasile. "Using genetic algorithms to evolve type-2 fuzzy logic systems for predicting bankruptcy." Kybernetes 46, no. 1 (January 9, 2017): 142–56. http://dx.doi.org/10.1108/k-06-2016-0152.

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Purpose Type-2 fuzzy sets became attractive in practice because of their footprint of uncertainty that gives them more degrees of freedom. This paper aims to use genetic algorithms (GAs) to design an interval Type-2 fuzzy logic system (IT2FLS) for the purpose of predicting bankruptcy. Design/methodology/approach The shape of type-2 membership functions, the parameters giving their spread and location in the fuzzy partitions and the set of fuzzy rules are evolved at the same time by encoding all together into the chromosome representation. The enhanced Karnik–Mendel algorithms are used for the centroid type-reduction and defuzzification stage. The performance in predicting bankruptcy is evaluated by benchmarking IT2FLSs against type-1 FLSs. The experimental setup consists of evolving 100 configurations for both the T1FLS and IT2FLS and comparing their in-sample and out-of-sample average accuracy. Findings The experiments confirm that representing and capturing uncertainty with more degrees of freedom is an important advantage. It is this extra potential of IT2FLSs that allows them to outperform T1FLS, especially in terms of generalization capability. Originality/value The strategy followed in this paper is to train an IT2FLS from scratch rather than tuning the parameters of an existing T1FLS. Because this leads to solving a mixed integer optimization problem, the GA-based approach is specifically designed and uses genetic operators that are most suited for such a case: tournament selection, extended Laplace crossover and power mutation. Finally, the trained IT2FLS is applied to bankruptcy prediction, and its generalization capability is compared with related techniques.

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Journal articles on the topic 'Genetic algorithms and fuzzy logic'

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