Relevant bibliographies by topics / Multiobjective genetic fuzzy systems

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Multiobjective genetic fuzzy systems'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Multiobjective genetic fuzzy systems"

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Vijayakumar, K. "Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/962402.

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Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sortin
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Shahsavari Pour, N., H. Asadi, and M. Pour Kheradmand. "Fuzzy Multiobjective Traffic Light Signal Optimization." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/249726.

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Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the
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Antonelli, Michela, Pietro Ducange, and Francesco Marcelloni. "Genetic Training Instance Selection in Multiobjective Evolutionary Fuzzy Systems: A Coevolutionary Approach." IEEE Transactions on Fuzzy Systems 20, no. 2 (2012): 276–90. http://dx.doi.org/10.1109/tfuzz.2011.2173582.

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Kiyota, T., Y. Tsuji, and E. Kondo. "Unsatisfying functions and multiobjective fuzzy satisficing design using genetic algorithms." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 33, no. 6 (2003): 889–97. http://dx.doi.org/10.1109/tsmcb.2003.810899.

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Zolfagharian, Ali, Amin Noshadi, Seyed Ebrahim Ghasemi, and Mohd Zarhamdy Md Zain. "A nonparametric approach using artificial intelligence in vibration and noise reduction of flexible systems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 8 (2013): 1329–47. http://dx.doi.org/10.1177/0954406213508510.

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The main aim of this paper is to broaden the application’s area of artificial intelligence including fuzzy logic and multiobjective evolutionary algorithm into real-time control area. Wiper system is a high order, nonlinear model with single-input and multi-outputs so that rise time, maximum overshoot, and end-point vibration of wiper blade are observed in conflict as the faster response leads to the larger level of undesired noise and vibration. The first part of this paper centers acquiring experimental data from a passenger automobile wiper system during its operation and using a reliable n
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Trebi-Ollennu, A., and B. A. White. "Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design." IEE Proceedings - Control Theory and Applications 144, no. 2 (1997): 137–42. http://dx.doi.org/10.1049/ip-cta:19971031.

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Sakawa, Masatoshi, and Kosuke Kato. "An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures." Advances in Operations Research 2009 (2009): 1–17. http://dx.doi.org/10.1155/2009/372548.

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We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving pr
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Sakawa, Masatoshi, and Ryo Kubota. "Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms." European Journal of Operational Research 120, no. 2 (2000): 393–407. http://dx.doi.org/10.1016/s0377-2217(99)00094-6.

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Ying-Yi Hong and Wei-Fen Huang. "Interactive multiobjective passive filter planning with fuzzy parameters in distribution systems using genetic algorithms." IEEE Transactions on Power Delivery 18, no. 3 (2003): 1043–50. http://dx.doi.org/10.1109/tpwrd.2003.813630.

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Zhang, Limei, Honglei Yang, Jing Lv, Yongfu Liu, and Wei Tang. "Multiobjective Optimization Approach for Coordinating Different DG from Distribution Network Operator." Journal of Electrical and Computer Engineering 2018 (November 15, 2018): 1–13. http://dx.doi.org/10.1155/2018/4790942.

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Integrating with analysis of uncertainties, this paper presented a multiobjective optimization approach for coordinating different DG from the perspective of Distribution Network Operator (DISOPER). Aiming to three uncertain factors including fuzzy variable, random variable, and interval variable, the information entropy and interval analysis methods are adopted to construct multistate models of multisource uncertainty. The information entropy method is to convert fuzzy variable into equivalent random variable. Interval analysis method is to transform random variables into interval variables b
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Dissertations / Theses on the topic "Multiobjective genetic fuzzy systems"

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Cárdenas, Edward Hinojosa. "Geração genética multiobjetivo de sistemas fuzzy usando a abordagem iterativa." Universidade Federal de São Carlos, 2011. https://repositorio.ufscar.br/handle/ufscar/486.

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Made available in DSpace on 2016-06-02T19:05:54Z (GMT). No. of bitstreams: 1 3998.pdf: 3486824 bytes, checksum: f1c040adfdc7d0672bc93a058f8a413d (MD5) Previous issue date: 2011-06-28<br>Financiadora de Estudos e Projetos<br>The goal of this work is to study, expand and evaluate the use of multiobjective genetic algorithms and the iterative rule learning approach in fuzzy system generation, especially, in fuzzy rule-based systems, both in automatic fuzzy rule generation from datasets and in fuzzy sets optimization. This work investigates the use of multi-objective genetic algorithms with a fo
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Pimenta, Adinovam Henriques de Macedo. "Geração genética multiobjetivo de bases de conhecimento fuzzy com enfoque na distribuição das soluções não dominadas." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/8574.

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Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-02-14T11:18:13Z No. of bitstreams: 1 TeseAHMP.pdf: 2470407 bytes, checksum: b3f2c2d64bfa00285c28963c74627bea (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-03-20T13:12:18Z (GMT) No. of bitstreams: 1 TeseAHMP.pdf: 2470407 bytes, checksum: b3f2c2d64bfa00285c28963c74627bea (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-03-20T13:12:31Z (GMT) No. of bitstreams: 1 TeseAHMP.pdf: 2470407 bytes, checksum: b3f2c2d64bfa00285c28963c74627bea (MD5)<br>Made availabl
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Shook, David Adam. "Control of a benchmark structure using GA-optimized fuzzy logic control." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1088.

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Pires, Danúbia Soares. "PROPOSTA DE CONTROLE NEBULOSO BASEADO EM CRITÉRIO DE ESTABILIDADE ROBUSTA NO DOMÍNIO DO TEMPO DISCRETO VIA ALGORITMO GENÉTICO MULTIOBJETIVO." Universidade Federal do Maranhão, 2013. http://tedebc.ufma.br:8080/jspui/handle/tede/505.

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Made available in DSpace on 2016-08-17T14:53:25Z (GMT). No. of bitstreams: 1 dissertacao Danubia.pdf: 4153198 bytes, checksum: f8dd2d9ab5e8fbfa00744bcff5dce73b (MD5) Previous issue date: 2013-09-30<br>In this master thesis, a robust fuzzy digital PID control methodology based on gain and phase margins specifications, is proposed. A mathematical formulation, based on gain and phase margins specifications, the Takagi-Sugeno fuzzy model of the plant to be controlled, the structure of the digital PID controller and the time delay uncertain system, was developed. From input and output data of t
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Lima, Helano Póvoas de. "Uma abordagem para construção de sistemas fuzzy baseados em regras integrando conhecimento de especialistas e extraído de dados." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/7224.

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Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-09-15T12:10:51Z No. of bitstreams: 1 DissHPL.pdf: 5127660 bytes, checksum: 4ffaa3ce20b9eb7adef78d152d5c17d2 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-16T19:47:09Z (GMT) No. of bitstreams: 1 DissHPL.pdf: 5127660 bytes, checksum: 4ffaa3ce20b9eb7adef78d152d5c17d2 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-16T19:47:15Z (GMT) No. of bitstreams: 1 DissHPL.pdf: 5127660 bytes, checksum: 4ffaa3ce20b9eb7adef78d152d5c17d2 (MD5)<br>Made available
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Arnett, Timothy J. "Verification of Genetic Fuzzy Systems." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1460731645.

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Xu, Li Da. "Fuzzy multiobjective mathematical programming in economic systems analysis: design and method." PDXScholar, 1986. https://pdxscholar.library.pdx.edu/open_access_etds/471.

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Economic systems analysis is a systems analysis technique of setting out the factors that have to be taken into account in making economic systems decisions. The inquiring and operational systems of the technique are almost exclusively designed for well-structured systems. In review of economic systems analysis against systems thinking, there is a growing tendency to discard the analytical approach as inappropriate for dealing with an ill-structured issue. Therefore, economic systems analysis needs both the inquiring and operational systems which are appropriate for ill-structured systems. The
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Fonseca, Carlos Manuel Mira da. "Multiobjective genetic algorithms with application to control engineering problems." Thesis, University of Sheffield, 1995. http://etheses.whiterose.ac.uk/1887/.

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Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree
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Stockton, Nicklas O. "Hybrid Genetic Fuzzy Systems for Control of Dynamic Systems." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523635312922039.

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LI, Haibing. "Financial fraud detection by using grammar-based multiobjective genetic programming with ensemble learning." Digital Commons @ Lingnan University, 2015. https://commons.ln.edu.hk/cds_etd/13.

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Financial fraud is a criminal act, which violates the law, rules or policy to gain unauthorized financial benefit. As an increasingly serious problem, it has attracted a lot of concerns. The major consequences are loss of billions of dollars each year, investor confidence and corporate reputation. Therefore, a study area called Financial Fraud Detection (FFD) is obligatory, in order to prevent the destructive results caused by financial fraud. In general, traditional modeling approaches are applied and based on pre-defined hypothesis testing of causes and effects for FFD problems. In addition,
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Books on the topic "Multiobjective genetic fuzzy systems"

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Sakawa, Masatoshi. Genetic Algorithms and Fuzzy Multiobjective Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1519-7.

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1959-, Nishizaki Ichiro, and Katagiri Hideki, eds. Fuzzy stochastic multiobjective programming. Springer, 2011.

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Sakawa, Masatoshi. Large Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches. Physica-Verlag HD, 2000.

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Pawar, Prashant M. Structural health monitoring using genetic fuzzy systems. Springer, 2011.

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Pawar, Prashant M., and Ranjan Ganguli. Structural Health Monitoring Using Genetic Fuzzy Systems. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-907-9.

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Islam, Rafiqul. Genetic algorithms, fuzzy systems, and website classification. Nova Science Publishers, 2011.

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Roman, Słowiński, and Teghem Jacques 1948-, eds. Stochastic versus fuzzy approaches to multiobjective mathematical programming under uncertainty. Kluwer Academic, 1990.

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Oscar, Cordón, ed. Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, 2001.

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1944-, Sanchez Elie, Shibata Takanori 1967-, and Zadeh Lotfi Asker, eds. Genetic algorithms and fuzzy logic systems: Soft computing perspectives. World Scientific Pub., 1997.

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Adeli, Hojjat. Machine learning: Neural networks, genetic algorithms, and fuzzy systems. Wiley, 1995.

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Book chapters on the topic "Multiobjective genetic fuzzy systems"

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Ishibuchi, Hisao, and Yusuke Nojima. "Multiobjective Genetic Fuzzy Systems." In Intelligent Systems Reference Library. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01799-5_5.

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Ishibuchi, Hisao, and Yusuke Nojima. "Multiobjective Genetic Fuzzy Systems." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_77.

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Lee, Michael A., and Henrik Esbensen. "Fuzzy/Multiobjective Genetic Systems for Intelligent Systems Design Tools and Components." In Fuzzy Evolutionary Computation. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4_3.

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Pytel, Krzysztof, and Tadeusz Nawarycz. "The Fuzzy-Genetic System for Multiobjective Optimization." In Swarm and Evolutionary Computation. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_38.

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Pires, Danúbia Soares, and Ginalber Luiz de Oliveira Serra. "Multiobjective Genetic Fuzzy PID Control Design for Uncertain Dynamic Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34654-5_34.

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Cordón, Oscar, María José Del Jesus, Francisco Herrera, Luis Magdalena, and Pedro Villar. "A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems." In Interpretability Issues in Fuzzy Modeling. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-37057-4_4.

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Nojima, Yusuke, Yuji Takahashi, and Hisao Ishibuchi. "Application of Parallel Distributed Implementation to Multiobjective Fuzzy Genetics-Based Machine Learning." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15702-3_45.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Two-Stage Fuzzy Clustering." In Multiobjective Genetic Algorithms for Clustering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_7.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay, and Anirban Mukhopadhyay. "Multiobjective Genetic Algorithm-Based Fuzzy Clustering." In Multiobjective Genetic Algorithms for Clustering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_5.

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Geyer-Schulz, Andreas. "Fuzzy Genetic Algorithms." In Fuzzy Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5505-6_12.

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Conference papers on the topic "Multiobjective genetic fuzzy systems"

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Niwa, Keiichi, Tomohiro Hayashida, and Masatoshi Sakawa. "Multiobjective two-level 0–1 programming through distributed genetic algorithms." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007359.

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Ishibuchi, Hisao. "Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295487.

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Nojima, Yusuke, and Hisao Ishibuchi. "Multiobjective genetic fuzzy rule selection with fuzzy relational rules." In 2013 IEEE 6th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS 2013). IEEE, 2013. http://dx.doi.org/10.1109/gefs.2013.6601056.

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Edward Hinojosa Cardenas and Heloisa A. Camargo. "Multiobjective genetic generation of fuzzy classifiers using the iterative rule learning." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251183.

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Alcala, Rafael, Yusuke Nojima, Francisco Herrera, and Hisao Ishibuchi. "Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277369.

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Brar, Y. S., J. S. Dhillon, and D. P. Kothari. "Multiobjective Load Dispatch Based on Genetic-Fuzzy Technique." In 2006 IEEE PES Power Systems Conference and Exposition. IEEE, 2006. http://dx.doi.org/10.1109/psce.2006.296438.

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Serra, Ginalber, and Celso Bottura. "Genetic Approach for Neural Scheduling of Multiobjective Fuzzy PI Controllers." In 2006 International Symposium on Evolving Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/isefs.2006.251147.

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Ishibuchi, Hisao, Yusuke Nakashima, and Yusuke Nojima. "Search ability of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277370.

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Kamkar, Iman, and Mohammad-R. Akbarzadeh-T. "Multiobjective cellular genetic algorithm with adaptive fuzzy fitness granulation." In 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2010. http://dx.doi.org/10.1109/icsmc.2010.5642401.

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Cordon, Oscar, Enrique Herrera-Viedma, and Maria Luque. "Fuzzy Linguistic Query-based User Profile Learning by Multiobjective Genetic Algorithms." In 2006 International Symposium on Evolving Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/isefs.2006.251152.

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Reports on the topic "Multiobjective genetic fuzzy systems"

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Xu, Li. Fuzzy multiobjective mathematical programming in economic systems analysis: design and method. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.471.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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