Relevant bibliographies by topics / Genetic algorithms and fuzzy logic

Academic literature on the topic 'Genetic algorithms and fuzzy logic'

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Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "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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Dissertations / Theses on the topic "Genetic algorithms and fuzzy logic"

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Karaboga, Dervis. "Design of fuzzy logic controllers using genetic algorithms." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296639.

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Tsang, Yiu-ming. "Intelligent polishing using fuzzy logic and genetic algorithm." View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37206400.

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Tsang, Yiu-ming, and 曾耀明. "Intelligent polishing using fuzzy logic and genetic algorithm." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38589291.

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Mahmoud, Abdallah Abdel-Rahman Hassan. "Identification of human gait using genetic algorithms tuned fuzzy logic." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Cuddy, Steven John. "The development of genetic algorithms and fuzzy logic for geoscience applications." Thesis, University of Aberdeen, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275019.

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This thesis describes how I have researched and developed new methods for the prediction of rock physical properties using genetic algorithms and fuzzy logic (GAFL).  These techniques are improvements on conventional methods providing two original but dissimilar tools for formation evaluation and reservoir characterisation. The premise behind the use of fuzzy logic in this context is that a reservoir can be broken down into several lithotypes, each having characteristic statistical distributions for electrical log values.  Fuzzy logic attempt to uncover the relationships between these distributions.  Genetic algorithms use a feedback technique that assumes a continuous functional relationship between the electrical log values and rock properties, generating and testing equations that fit predicted and observed responses.  Complex non-linear equations are “evolved” until the best fit is obtained.  Genetic algorithms provide the functional form of the equation as well as the constant parameters of the relationship. I have modified conventional GAFL techniques so that they can be more precisely calibrated and applied to geoscience problems more successfully.  This research analysed the characteristics of large data sets from several North Sea and Middle Eastern fields, and led to the design of software that automatically calibrates GAFL in a way that is less sensitive to noise and data outliers.  I describe the applications of these new techniques to permeability, litho-facies, porosity and shear velocity prediction;  the repair of poor electrical logs and the modelling of shaly sand equations. Permeability governs the movement of fluids through reservoir rocks and is therefore a critical input into reservoir models.  Permeability estimation is extremely challenging, as it is difficult to measure directly using current sub-surface logging technology.  GAFL was applied to predict permeability in the Northern North Sea oil fields.  The newly developed software provides an important and visual indication of the uncertainty associated with the predicted permeabilities.
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Chwee, Ng Kim. "Switching control systems and their design via genetic algorithms." Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361268.

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Griffiths, Ian. "Microcontroller implementation of artificial intelligence for autonomous guided vehicles." Thesis, University of Wolverhampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266837.

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McClintock, Shaunna. "Soft computing : a fuzzy logic controlled genetic algorithm environment." Thesis, University of Ulster, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268579.

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Wong, King-sau, and 黃敬修. "Improving the performance of lifts using artificial intelligence techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2768295X.

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(Uncorrected OCR) Abstract of thesis entitled Improving the Performance of Lifts Using Artificial Intelligence Techniques submitted by Wong King Sau for the degree of Doctor of Philosophy at the University of Hong Kong in August 2003 An elevator group control system manages multiple elevators to serve hall calls in a building. Most elevator group control systems need to recognize the traffic pattern of the building and then change their control algorithms to improve the efficiency of the elevator system. However, the traffic flow in a building is very difficult to be classified into distinct patterns. Traffic recognition systems can recognize certain traffic patterns, but mixed traffic patterns are difficult to be recognized. The aim of this study was therefore to develop improved duplex elevator group control systems that do not need to recognize the traffic pattern. A fuzzy logic. control unit and genetic algorithms control unit were used. A fuzzy logic control unit integrates with the conventional duplex elevator group control system to improve performance especially in mixed traffic patterns with intermittent heavy traffic demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building. The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator. Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system. (An abstract of 297 words.) ~ Signed _ Wong King Sau
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鄺世凌 and Sai-ling Kwong. "Evolutionary design of fuzzy-logic controllers for manufacturing systems with production time-delays." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B3124323X.

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Books on the topic "Genetic algorithms and fuzzy logic"

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Genetic algorithms and fuzzy multiobjective optimization. Boston: Kluwer Academic Publishers, 2002.

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Furuhashi, Takeshi, ed. Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60607-6.

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Ruan, Da. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms. Boston, MA: Springer US, 1997.

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Fuzzy modeling and genetic algorithms for data mining and exploration. San Francisco, CA: Elsevier/Morgan Kaufmann, 2004.

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Adeli, Hojjat. Cost optimization of structures: Fuzzy logic, genetic algorithms, and parallel computing. Chichester, England: Wiley, 2006.

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McClintock, Shaunna. Soft computing: A fuzzy logic controlled genetic algorithm environment. [S.l: The Author], 1999.

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Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Soft computing in water resources engineering: Artificial neural networks, fuzzy logic and genetic algorithms. Southampton: WIT Press, 2012.

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IEEE/Nagoya University World Wisepersons Workshop (1994 Nagoya-shi, Japan). Advances in fuzzy logic, neural networks, and genetic algorithms: IEEE/Nagoya University World Wisepersons Workshop, Nagoya, Japan, August 9-10, 1994 : selected papers. Berlin: Springer, 1995.

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Book chapters on the topic "Genetic algorithms and fuzzy logic"

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Buckley, James J., and Esfandiar Eslami. "Genetic Algorithms." In An Introduction to Fuzzy Logic and Fuzzy Sets, 253–60. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1799-7_15.

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Castillo, Oscar, and Luis T. Aguilar. "Genetic Algorithms." In Type-2 Fuzzy Logic in Control of Nonsmooth Systems, 23–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03134-3_2.

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Tagliasacchi, Marco. "Optical Flow Estimation Using Genetic Algorithms." In Fuzzy Logic and Applications, 309–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/10983652_37.

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Albrecht, R. F. "Topological Approach to Fuzzy Sets and Fuzzy Logic." In Artificial Neural Nets and Genetic Algorithms, 1–7. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6384-9_1.

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Quafafou, Mohamed, and Mohammed Nafia. "GAITS: Fuzzy sets-based algorithms for computing strategies using genetic algorithms." In Fuzzy Logic in Artificial Intelligence, 59–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56920-0_8.

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Pappalardo, F., E. Mastriani, P. L. Lollini, and S. Motta. "Genetic Algorithm Against Cancer." In Fuzzy Logic and Applications, 223–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_27.

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Alander, Jarmo T. "An Indexed Bibliography of Genetic Algorithms with Fuzzy Logic." In Fuzzy Evolutionary Computation, 299–318. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_13.

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Cococcioni, Marco, Pierluigi Guasqui, Beatrice Lazzerini, and Francesco Marcelloni. "Identification of Takagi-Sugeno Fuzzy Systems Based on Multi-objective Genetic Algorithms." In Fuzzy Logic and Applications, 172–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_21.

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de Souza, André Nunes, Ivan Nunes da Silva, and José Alfredo Covolan Ulson. "A Fuzzy Logic System Applied in Lightning Models." In Artificial Neural Nets and Genetic Algorithms, 356–58. Vienna: Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_88.

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Ciaramella, Angelo, Witold Pedrycz, and Roberto Tagliaferri. "OR/AND Neurons for Fuzzy Set Connectives Using Ordinal Sums and Genetic Algorithms." In Fuzzy Logic and Applications, 188–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_23.

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Conference papers on the topic "Genetic algorithms and fuzzy logic"

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Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14231.

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Abstract Nelder and Mead Simplex (NMS) algorithm is an effective nonlinear programming technique. Trabia and Lu (1999) recently presented a novel algorithm, Fuzzy Simplex (FS), which improved the efficiency of Nelder and Mead Simplex by using fuzzy logic to determine the orientation and size of the simplex. While Fuzzy Simplex algorithm can be successfully used to search a wide variety of functions, it suffers, as other simplex algorithms, from its dependence on the initial guess and the original simplex size. This paper addresses this problem by combining the Fuzzy Simplex with Genetic Algorithm (GA) in a hybrid algorithm. Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The Hybrid GA Fuzzy Simplex algorithm generally results in a faster convergence.
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Mahmood, A. "A genetic algorithm based fuzzy logic controller for non-linear systems." In Second International Conference on Genetic Algorithms in Engineering Systems. IEE, 1997. http://dx.doi.org/10.1049/cp:19971151.

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Shill, Pintu Chandra, Md Faijul Amin, M. A. H. Akhand, and Kazuyuki Murase. "Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251207.

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Parimi, V. Ram Mohan, and Devendra P. Garg. "Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60502.

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This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.
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Bajrami, Xhevahir, Artan Dermaku, Nysret Demaku, Sali Maloku, Adem Kikaj, and Agon Kokaj. "Genetic and Fuzzy logic algorithms for robot path finding." In 2016 5th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2016. http://dx.doi.org/10.1109/meco.2016.7525739.

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Pelusi, D. "Optimization of a fuzzy logic controller using genetic algorithms." In 2011 International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2011. http://dx.doi.org/10.1109/ihmsc.2011.105.

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Pearce, R. "Use of fuzzy logic to overcome constant problems in genetic algorithms." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951017.

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Jannson, Tomasz P., Andrew A. Kostrzewski, Igor V. Ternovskiy, and Dai Hyun Kim. "Fuzzy logic genetic algorithm for hypercompression." In Optical Science, Engineering and Instrumentation '97, edited by Bruno Bosacchi, James C. Bezdek, and David B. Fogel. SPIE, 1997. http://dx.doi.org/10.1117/12.284216.

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Castillo-Ortega, Rita, Nicolas Marin, Daniel Sanchez, and Andrea G. B. Tettamanzi. "Linguistic Summarization of Time Series Data using Genetic Algorithms." In 7th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.145.

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Song, Y. H. "Improved genetic algorithms with fuzzy logic controlled crossover and mutation." In UKACC International Conference on Control. Control '96. IEE, 1996. http://dx.doi.org/10.1049/cp:19960541.

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Reports on the topic "Genetic algorithms and fuzzy logic"

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Smith, James F., Rhyne III, and II Robert D. Optimal Allocation of Distributed Resources Using Fuzzy Logic and a Genetic Algorithm. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada384792.

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Ankenbrandt, C. A., B. P. Buckles, F. E. Petry, and M. Lybanon. Ocean Feature Recognition Using Genetic Algorithms with Fuzzy Fitness Functions (GA/F3). Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada230891.

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