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Gotowe bibliografie tematyczne / Genetic algorithms and fuzzy logic / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Genetic algorithms and fuzzy logic”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 lutego 2022

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1

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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2

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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3

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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4

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 m

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5

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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6

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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7

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 expe

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8

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 perfor

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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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

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21

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 no

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22

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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23

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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24

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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25

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 assistan

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26

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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27

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.

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28

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 o

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29

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 algorith

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30

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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31

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

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32

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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33

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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35

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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36

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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37

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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38

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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39

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 t

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40

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 Gene

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41

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. Gaussia

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42

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. Diff

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43

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

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44

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 mod

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48

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 l

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49

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 us

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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

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