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Relevant bibliographies by topics / Evolutionary computing; Fuzzy logic

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Academic literature on the topic 'Evolutionary computing; Fuzzy logic'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Evolutionary computing; Fuzzy logic"

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Salman, Afan Galih. "Pemodelan Sistem Fuzzy Dengan Menggunakan Matlab." ComTech: Computer, Mathematics and Engineering Applications 1, no. 2 (December 1, 2010): 276. http://dx.doi.org/10.21512/comtech.v1i2.2349.

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Fuzzy logic is a method in soft computing category, a method that could process uncertain, inaccurate, and less cost implemented data. Some methods in soft computing category besides fuzzy logic are artificial network nerve, probabilistic reasoning, and evolutionary computing. Fuzzy logic has the ability to develop fuzzy system that is intelligent system in uncertain environment. Some stages in fuzzy system formation process is input and output analysis, determining input and output variable, defining each fuzzy set member function, determining rules based on experience or knowledge of an expert in his field, and implementing fuzzy system. Overall, fuzzy logic uses simple mathematical concept, understandable, detectable uncertain and accurate data. Fuzzy system could create and apply expert experiences directly without exercise process and effort to decode the knowledge into a computer until becoming a modeling system that could be relied on decision making.

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Ekong, Victor. "SOFT COMPUTING SYSTEM FOR THE DIAGNOSIS OF HORMONAL IMBALANCE." Transactions on Machine Learning and Artificial Intelligence 7, no. 6 (January 8, 2020): 30–42. http://dx.doi.org/10.14738/tmlai.76.7507.

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Soft computing, as a science of modelling systems, applies techniques such as evolutionary computing, fuzzy logic, and their hybrids to solve real life problems. Soft computing techniques are quite tolerant to incomplete, imprecise, and uncertainty when dealing with complex situations. This study adopts a hybrid of genetic algorithm and fuzzy logic in diagnosing hormonal imbalance. Hormones are chemical messengers that are vital for growth, reproduction, and are essential for human existence. Hormones may sometimes not be balanced which is a medical condition that often go unnoticed and it’s quite difficult to be diagnosed by medical experts. Hormonal imbalance has several symptoms that could also be confused for other ailments. This proposed system serves as support for medical experts to improve the precision of diagnosis of hormonal imbalance. The study further demonstrates the effective hybridization of genetic algorithm and fuzzy logic in resolving human problems.

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Sadiku, Matthew N. O., Yonghui Wang, Suxia Cui, and Sarhan M. Musa. "SOFT COMPUTING: AN INTRODUCTION." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 6 (June 30, 2018): 63. http://dx.doi.org/10.23956/ijarcsse.v8i6.615.

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Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

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CHEETHAM, WILLIAM, SIMON SHIU, and ROSINA O. WEBER. "Soft case-based reasoning." Knowledge Engineering Review 20, no. 3 (September 2005): 267–69. http://dx.doi.org/10.1017/s0269888906000579.

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The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

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Rahman, Muhammad Muhitur, Md Shafiullah, Syed Masiur Rahman, Abu Nasser Khondaker, Abduljamiu Amao, and Md Hasan Zahir. "Soft Computing Applications in Air Quality Modeling: Past, Present, and Future." Sustainability 12, no. 10 (May 14, 2020): 4045. http://dx.doi.org/10.3390/su12104045.

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Air quality models simulate the atmospheric environment systems and provide increased domain knowledge and reliable forecasting. They provide early warnings to the population and reduce the number of measuring stations. Due to the complexity and non-linear behavior associated with air quality data, soft computing models became popular in air quality modeling (AQM). This study critically investigates, analyses, and summarizes the existing soft computing modeling approaches. Among the many soft computing techniques in AQM, this article reviews and discusses artificial neural network (ANN), support vector machine (SVM), evolutionary ANN and SVM, the fuzzy logic model, neuro-fuzzy systems, the deep learning model, ensemble, and other hybrid models. Besides, it sheds light on employed input variables, data processing approaches, and targeted objective functions during modeling. It was observed that many advanced, reliable, and self-organized soft computing models like functional network, genetic programming, type-2 fuzzy logic, genetic fuzzy, genetic neuro-fuzzy, and case-based reasoning are rarely explored in AQM. Therefore, the partially explored and unexplored soft computing techniques can be appropriate choices for research in the field of air quality modeling. The discussion in this paper will help to determine the suitability and appropriateness of a particular model for a specific modeling context.

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Tanaka, Kazuo. "Advanced Computational Intelligence in Control Theory and Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 2 (April 20, 1999): 67. http://dx.doi.org/10.20965/jaciii.1999.p0067.

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We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

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Singh, Sarabjeet, Satvir Singh, and Vijay Kumar Banga. "An Interval Type 2 Fuzzy Logic Framework for Faster Evolutionary Design." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5140–48. http://dx.doi.org/10.1166/jctn.2019.8576.

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In this paper, a fast processing and efficient framework has been proposed to get an optimum output from a noisy data set of a system by using interval type-2 fuzzy logic system. Further, the concept of GPGPU (General Purpose Computing on Graphics Processing Unit) is used for fast execution of the fuzzy rule base on Graphics Processing Unit (GPU). Application of Whale Optimization Algorithm (WOA) is used to ascertain optimum output from noisy data set. Which is further integrated with Interval Type-2 (IT2) fuzzy logic system and executed on Graphics Processing Unit for faster execution. The proposed framework is also designed for parallel execution using GPU and the results are compared with the serial program execution. Further, it is clearly observed that the parallel execution rule base evolved provide better accuracy in less time. The proposed framework (IT2FLS) has been validated with classical bench mark problem of Mackey Glass Time Series. For non-stationary time-series data with additive gaussian noise has been implemented with proposed framework and with T1 FLS. Further, it is observed that IT2 FLS provides better rule base for noisy data set.

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SAFFIOTTI, ALESSANDRO. "Using fuzzy logic for autonomous robotics: an on-line workshop." Knowledge Engineering Review 12, no. 01 (January 1997): 91–94. http://dx.doi.org/10.1017/s0269888997000040.

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In October 1995, Takeshi Furuhashi and his collegues at the Bio-Electronics Laboratory of Nagoya University, Japan, organized the first of a series of on-line workshops, held entirely on the World Wide Web. The advertised advantages of the on-line format were to allow fruitful exchanges while avoiding physical travel, and to guarantee wide visibility of the discussion. The first two workshops in the series were devoted to evolutionary computation; they can be accessed on the web at http://www.bioele.nuee.nagoya-u.ac.jp. The third workshop, named “First On-Line Workshop on Soft Computing” (WSC1), had a broader scope, including all the techniques that go under the heading of “soft computing”, like fuzzy logic, neuro computing, genetic computing, and so on. WSC1 took place from August 19 to 30 1996, and it is accessible on the web at http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/. Because the declared goal of an on-line workshop is to prompt discussion, the rules for submission were looser than in most traditional workshops: papers were not subject to peer review, and it was possible to submit already published papers. All the submitted papers were made visible on the web one week before the workshop, and people could send comments and questions by email during the two workshop weeks; all the questions, comments, and authors' replies are also visible at the WSC1 web site.

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Кричевский, Михаил, and Mihail Krichevskiy. "Selection of the Staff With the Use of Soft Computing." Management of the Personnel and Intellectual Resources in Russia 6, no. 6 (January 23, 2018): 61–65. http://dx.doi.org/10.12737/article_5a4624634bb683.14483599.

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In a changing environment and inaccurate information, it is difficult to get an unambiguous answer about the quality of the candidate for the position, based only on the results of viewing the applicant’s questionnaires. As a consequence, recently there has been a trend towards the use of soft computing (neural networks, fuzzy logic and evolutionary computations) in tasks personnel’s selection. The article presents the solution of such a problem using the methods of soft computing for a software company. We use a neural-fuzzy system such as the ANFIS (Adaptive Network-Based Fuzzy Inference System) to quantify the candidate’s quality. The idea of neural-fuzzy systems is to determine the parameters of fuzzy systems through training methods used in neural networks. The most important advantage of this system lies in the automatic creation of the rules base. After completing the training, we receive an assessment of the quality of the candidate in the form of a scoring on a 10-point scale. In addition, we derive a regression equation that relates the candidate’s quality with the input variables.

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Hirota, Toshio Fukudand Kaoru. "Message from Editors-in-Chief." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 1 (October 20, 1997): 0. http://dx.doi.org/10.20965/jaciii.1997.p0000.

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We are very pleased and honored to have an opportunity to publish a new journal the "International Journal of Advanced Computational Intelligence" (JACI). The JACI is a new, bimonthly journal covering the field of computer science. This journal focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and evolutionary computations, in order to assist in fostering the application of intelligent systems to industry. This new field is called computational intelligence or soft computing. It has already been studied by many researchers, but no single, integrated journal exists anywhere in the world. This new journal gives readers the state of art of the theory and application of Advanced Computational Intelligence. The Topics include, but are not limited to: Fuzzy Logic, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Network Systems, Multimedia, the Human Interface, Biologically-Inspired Evolutionary Systems, Artificial Life, Chaos, Fractal, Wavelet Analysis, Scientific Applications and Industrial Applications. The journal, JACI, is supported by many researchers and scientific organizations, e.g., the International Fuzzy Systems Association (IFSA), the Japan Society of Fuzzy Theory and Systems (SOFT), the Brazilian Society of Automatics (SBA) and The Society of Instrument and Control Engineers (SICE), and we are currently negotiating with the John von Neumann Computer Society (in Hungary). Our policy is to have world-wide communication with many societies and researchers in this field. We would appreciate it if those organizations and people who have an interest in co-sponsorship or have proposals for special issues in this journal, as well as paper submissions, could contact us. Finally our special thanks go to the editorial office of Fuji Technology Press Ltd., especially to its president, Mr. K. Hayashi, and to the editor, Mr. Y. Inoue, for their efforts in publishing this new journal. Lotti A. Zadeh The publication of the International Journal of Advanced Computational Intelligence (JACI) is an important milestone in the advancement of our understanding of how intelligent systems can be conceived, designed, built, and deployed. When one first hears of computational intelligence, a question that naturally arises is: What is the difference, if any, between computational intelligence (CI) and artificial intelligence (AI)? As one who has witnessed the births of both AI and CI, I should like to suggest an answer. As a branch of science and technology, artificial intelligence was born about four decades ago. From the outset, AI was based on classical logic and symbol manipulation. Numerical computations were not welcomed and probabilistic techniques were proscribed. Mainstream AI continued to evolve in this spirit, with symbol manipulation still occupying the center of the stage, but not to the degree that it did in the past. Today, probabilistic techniques and neurocomputing are not unwelcome, but the focus is on distributed intelligence, agents, man-machine interfaces, and networking. With the passage of time, it became increasing clear that symbol manipulation is quite limited in its ability to serve as a foundation for the design of intelligent systems, especially in the realms of robotics, computer vision, motion planning, speech recognition, handwriting recognition, fault diagnosis, planning, and related fields. The inability of mainstream AI to live up to expectations in these application areas has led in the mid-eighties to feelings of disenchantment and widespread questioning of the effectiveness of AI's armamentarium. It was at this point that the name computational intelligence was employed by Professor Nick Cercone of Simon Fraser University in British Columbia to start a new journal named Computational Intelligence -a journal that was, and still is, intended to provide a broader conceptual framework for the conception and design of intelligent systems than was provided by mainstream AI. Another important development took place. The concept of soft computing (SC) was introduced in 1990-91 to describe an association of computing methodologies centering on fuzzy logic (FL), neurocomputing (NC), genetic (or evolutionary) computing (GC), and probabilistic computing (PC). In essence, soft computing differs from traditional hard computing in that it is tolerant of imprecision, uncertainty and partial truth. The basic guiding principle of SC is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality. More recently, the concept of computational intelligence had reemerged with a meaning that is substantially different from that which it had in the past. More specifically, in its new sense, CI, like AI, is concerned with the conception, design, and deployment of intelligent systems. However, unlike mainstream AI, CI methodology is based not on predicate logic and symbol manipulation but on the methodologies of soft computing and, more particularly, on fuzzy logic, neurocomputing, genetic(evolutionary) computing, and probabilistic computing. In this sense, computational intelligence and soft computing are closely linked but not identical. In basic ways, the importance of computational intelligence derives in large measure from the effectiveness of the techniques of fuzzy logic, neurocomputing, genetic (evolutionary) computing, and probabilistic computing in the conception and design of information/intelligent systems, as defined in the statements of the aims and scope of the new journal of Advanced Computational Intelligence. There is one important aspect of both computational intelligence and soft computing that should be stressed. The methodologies which lie at the center of CI and SC, namely, FL, NC, genetic (evolutionary) computing, and PC are for the most part complementary and synergistic, rather than competitive. Thus, in many applications, the effectiveness of FL, NC, GC, and PC can be enhanced by employing them in combination, rather than in isolation. Intelligent systems in which FL, NC, GC, and PC are used in combination are frequently referred to as hybrid intelligent systems. Such systems are likely to become the norm in the not distant future. The ubiquity of hybrid intelligent systems is likely to have a profound impact on the ways in which information/intelligent systems are conceived, designed, built, and interacted with. At this juncture, the most visible hybrid intelligent systems are so-called neurofuzzy systems, which are for the most part fuzzy-rule-based systems in which neural network techniques are employed for system identification, rule induction, and tuning. The concept of neurofuzzy systems was originated by Japanese scientists and engineers in the late eighties, and in recent years has found a wide variety of applications, especially in the realms of industrial control, consumer products, and financial engineering. Today, we are beginning to see a widening of the range of applications of computational intelligence centered on the use of neurofuzzy, fuzzy-genetic, neurogenetic, neurochaotic and neuro-fuzzy-genetic systems. The editors-in-chief of Advanced Computational Intelligence, Professors Fukuda and Hirota, have played and are continuing to play majors roles both nationally and internationally in the development of fuzzy logic, soft computing, and computational intelligence. They deserve our thanks and congratulations for conceiving the International Journal of Advanced Computational Intelligence and making it a reality. International in both spirit and practice, JACI is certain to make a major contribution in the years ahead to the advancement of the science and technology of man-made information/intelligence systems -- systems that are at the center of the information revolution, which is having a profound impact on the ways in which we live, communicate, and interact with the real world. Lotfi A. Zadeh Berkeley, CA, July 24, 1997

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Dissertations / Theses on the topic "Evolutionary computing; Fuzzy logic"

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Leitch, Donald Dewar. "A new genetic algorithm for the evolution of fuzzy sets." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318473.

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張大任 and Tai-yam Cheung. "Evolutionary design of fuzzy-logic controllers for overhead cranes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31243010.

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Cheung, Tai-yam. "Evolutionary design of fuzzy-logic controllers for overhead cranes /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23636543.

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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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Rossiter, Jonathan Michael. "Humanist computing for knowledge discovery from ordered datasets." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300571.

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Wang, Haibin. "Interval neutrosophic sets and logic theory and applications in computing /." unrestricted, 2005. http://etd.gsu.edu/theses/available/etd-11172005-131340/.

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Thesis (Ph. D.)--Georgia State University, 2005.
1 electronic text (119 p. : ill.) : digital, PDF file. Title from title screen. Rajshekhar Sunderraman, committee chair; Yan-Qing Zhang, Anu Bourgeois, Lifeng Ding, committee members. Description based on contents viewed Apr. 3, 2007. Includes bibliographical references (p. 112-119).

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Wang, Haibin. "Interval Neutrosophic Sets and Logic: Theory and Applications in Computing." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/2.

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A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study.

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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.

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Soufian, Majeed. "Hard and soft computing techniques for non-linear modeling and control with industrial applications." Thesis, Manchester Metropolitan University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273053.

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Hill, Carla. "Mass assignments for inductive logic programming." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325748.

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Books on the topic "Evolutionary computing; Fuzzy logic"

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1950-, Adeli Hojjat, ed. Computational intelligence: Synergies of fuzzy logic, neural networks, and evolutionary computing. Chichester, West Sussex, United Kingdom: John Wiley & Sons, 2013.

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Zaus, Michael. Crisp and Soft Computing with Hypercubical Calculus: New Approaches to Modeling in Cognitive Science and Technology with Parity Logic, Fuzzy Logic, and Evolutionary Computing. Heidelberg: Physica-Verlag HD, 1999.

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Kacprzyk, Janusz, and Oscar Castillo. Evolutionary design of intelligent systems in modeling, simulation and control. Berlin: Springer, 2009.

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1964-, Feuring Thomas, ed. Fuzzy and neural: Interactions anmd applications. New York: Physica-Verlag, 1999.

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Chen, Guoqing. Fuzzy Logic and Soft Computing. Boston, MA: Springer US, 1999.

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Chen, Guoqing, Mingsheng Ying, and Kai-Yuan Cai, eds. Fuzzy Logic and Soft Computing. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5261-1.

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Petrosino, Alfredo, Vincenzo Loia, and Witold Pedrycz, eds. Fuzzy Logic and Soft Computing Applications. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52962-2.

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Furuhashi, Takeshi, and Yoshiki Uchikawa, eds. Fuzzy Logic, Neural Networks, and Evolutionary Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7.

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Zamuda, Aleš, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, and Bijaya Ketan Panigrahi, eds. Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37838-7.

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Tettamanzi, Andrea. Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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Book chapters on the topic "Evolutionary computing; Fuzzy logic"

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Gabryel, Marcin, and Leszek Rutkowski. "Evolutionary Designing of Logic-Type Fuzzy Systems." In Artifical Intelligence and Soft Computing, 143–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13232-2_17.

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Cerri, Stefano A., and Vincenzo Loia. "An Evolutionary View to the Design of Soft-Computing Agents." In Lectures on Soft Computing and Fuzzy Logic, 57–70. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1818-5_5.

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Dammavalam, Srinivasa Rao, Seetha Maddala, and M. H. M. Krishna Prasad. "Quality Evaluation Measures of Pixel - Level Image Fusion Using Fuzzy Logic." In Swarm, Evolutionary, and Memetic Computing, 485–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_59.

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Pal, Tandra. "Evolutionary Approaches to Rule Extraction for Fuzzy Logic Controllers." In Advances in Soft Computing — AFSS 2002, 421–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45631-7_57.

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Hannah, M. Esther, T. V. Geetha, and Saswati Mukherjee. "Automatic Extractive Text Summarization Based on Fuzzy Logic: A Sentence Oriented Approach." In Swarm, Evolutionary, and Memetic Computing, 530–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_63.

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Gadanayak, D. A., and Irani Majumder. "A Comparative Study of Two Types of Fuzzy Logic Controllers for Shunt Active Power Filters." In Swarm, Evolutionary, and Memetic Computing, 49–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_5.

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Valdez, Fevrier, Patricia Melin, and Oscar Castillo. "Fuzzy Logic for Parameter Tuning in Evolutionary Computation and Bio-inspired Methods." In Advances in Soft Computing, 465–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16773-7_40.

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Lin, T. Y. "Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing." In Rough Sets and Data Mining, 123–38. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1461-5_7.

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Madera, Quetzali, Mario Garcia, and Oscar Castillo. "Fuzzy Logic for Improving Interactive Evolutionary Computation Techniques for Ad Text Optimization." In Advances in Intelligent Systems and Computing, 291–300. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26211-6_25.

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Banerjee, Abhik, V. Mukherjee, and S. P. Ghoshal. "Real-Coded Genetic Algorithm and Fuzzy Logic Approach for Real-Time Load-Tracking Performance of an Autonomous Power System." In Swarm, Evolutionary, and Memetic Computing, 119–31. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03756-1_11.

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Conference papers on the topic "Evolutionary computing; Fuzzy logic"

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Omkar, S. N., Nikhil Ramaswamy, J. Senthilnath, S. Bharath, and N. S. Anuradha. "Gene Expression Programming-Fuzzy Logic Method for Crop Type Classification." In 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC). IEEE, 2012. http://dx.doi.org/10.1109/icgec.2012.97.

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Santhiya, M., and N. Pappa. "Evolutionary Algorithms based Fuzzy Logic Controller for Pressurized Water Nuclear Reactor." In 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT). IEEE, 2014. http://dx.doi.org/10.1109/icaccct.2014.7019428.

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HuaYun Yu and DaBin Zhang. "Design of Fuzzy Logic Controllers Based on Evolvable Hardware Platform." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.219.

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Touati, Youcef. "Fuzzy Logic-based Evolutionary Approach for Load Balancing in Large-Scale Wireless Sensor Networks." In 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2018. http://dx.doi.org/10.1109/uemcon.2018.8796811.

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Thakur, Reena, Vinay Kr Singh, and Manu Pratap Singh. "Evolutionary design of Fuzzy Logic Controllers with the techniques Artificial Neural Network and Genetic Algorithm for cart-pole problem." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705756.

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Hussian, Omnia S. S., Hany M. Elsayed, and M. A. Moustafa Hassan. "Fuzzy Logic Control for a Stand-Alone PV System with PI Controller for Battery Charging Based on Evolutionary Technique." In 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2019. http://dx.doi.org/10.1109/idaacs.2019.8924269.

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Guerrero, Maribel, Oscar Castillo, and Mario Garcia. "Fuzzy dynamic parameters adaptation in the Cuckoo Search Algorithm using fuzzy logic." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256923.

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Fredivianus, Nugroho, and Kurt Geihs. "Classifier systems with native fuzzy logic control operation." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3082487.

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Yi Hu, Peter Thomas, and Russel J. Stonier. "Traffic signal control using fuzzy logic and evolutionary algorithms." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424689.

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Preethi, J. "Temporal outlier detection using fuzzy logic and evolutionary computation." In 2013 International Conference on Optical Imaging Sensor and Security (ICOSS). IEEE, 2013. http://dx.doi.org/10.1109/icoiss.2013.6678404.

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Reports on the topic "Evolutionary computing; Fuzzy logic"

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Meitzler, Thomas, Regina Kistner, Bill Pibil, Euijung Sohn, Darryl Bryk, and David Bednarz. Computing the Probability of Target Detection in Dyanmic Visual Scenes Containing Clutter Using Fuzzy Logic Approach. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada576438.

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