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Готові списки джерел за темами / Evolutional computation

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Добірка наукової літератури з теми "Evolutional computation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Evolutional computation"

1

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Interactive Reduct Evolutional Computation for Aesthetic Design." Journal of Computing and Information Science in Engineering 5, no. 1 (2005): 1–7. http://dx.doi.org/10.1115/1.1846055.

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We propose a method of evolving designs based on the user’s personal preferences. The method works through an interaction between the user and a computer system. The method’s objective is to help the customer to set design parameters via a simple evaluation of displayed samples. An important feature is that the design attributes to which the user pays more attention (favored features) are estimated using reducts in rough set theory and reflected when refining the design. New design candidates are generated by the user’s evaluation of design samples generated at random. The values of attributes

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2

Yang, Cheng Zhong, and Xiao Shi Zheng. "A Video Characteristics Watermarking Algorithm Based on Bees Evolutional Computation." Applied Mechanics and Materials 571-572 (June 2014): 796–802. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.796.

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Digital video watermarking is an efficient way of copyright protection. A robust watermarking was proposed that ccording to the video features using the evolutional method of video image classification processing, suitable for video performance and human visual characteristics of video watermarking, and put forward a kind of using Bees evolutional algorithm for video image classification processing of video watermarking algorithm. All the key frame numbers are converts to a binary matrix after data transformation. At last the binary image is produced. From this, the watermarking image reflects

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3

Yar, Morteza Husainy, Vahid Rahmati, and Hamid Reza Dalili Oskouei. "A Survey on Evolutionary Computation: Methods and Their Applications in Engineering." Modern Applied Science 10, no. 11 (2016): 131. http://dx.doi.org/10.5539/mas.v10n11p131.

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Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It’s based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally catego

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4

Chen, Jun-Lang, and Chao-Qing Dai. "New Variable Separation Solutions of the (2+1)-Dimensional Generalized Broer-Kaup System." Zeitschrift für Naturforschung A 62, no. 12 (2007): 677–84. http://dx.doi.org/10.1515/zna-2007-1201.

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Using symbolic and algebra computation, the extended tanh-function method (ETM) based on the mapping method is further extended. New variable separation solutions of the (2+1)-dimensional generalized Broer-Kaup (GBK) system are derived. From the periodic wave solution and by selecting appropriate functions, the evolutional behaviours of dromions in the background of Jacobian elliptic wave and their interaction behaviours are investigated.

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5

Anderson, William, and Mohammad Farazmand. "Fast and scalable computation of shape-morphing nonlinear solutions with application to evolutional neural networks." Journal of Computational Physics 498 (February 2024): 112649. http://dx.doi.org/10.1016/j.jcp.2023.112649.

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6

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Design optimisation for customers' KANSEI requirement: application of interactive reduct evolutional computation to industrial design with curves." International Journal of Manufacturing Technology and Management 10, no. 4 (2007): 360. http://dx.doi.org/10.1504/ijmtm.2007.012155.

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7

Niu, Qun, Taijin Zhou, and Shiwei Ma. "A Quantum-Inspired Immune Algorithm for Hybrid Flow Shop with Makespan Criterion." JUCS - Journal of Universal Computer Science 15, no. (4) (2009): 765–85. https://doi.org/10.3217/jucs-015-04-0765.

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This paper presents a quantum-inspired immune algorithm (QIA) for Hybrid flow shop problems (HFSP) to minimize makespan. Since HFSP have been proved to be NP-hard in a strong sense when the objective is to minimize the makespan, an effective immune algorithm (IA) is used to solve the problems. IA is a kind of evolutional computation strategies, which is developed on the basis of a real immune mechanism in the human body, and has been employed to tackle complex scheduling problems and produce a reasonable manufacturing schedule. In order to achieve better results, the standard IA is combined wi

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8

MIYAGAWA, Kengo, Jiro SAKAMOTO, Satoshi KITAYAMA, and Nobutaka SHIMIZU. "Optimum design of cross-sectional sizes for lip groove-shaped steel considering compression and bending load by using evolutional computation." Proceedings of OPTIS 2018.13 (2018): 303. http://dx.doi.org/10.1299/jsmeoptis.2018.13.303.

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9

Whitley, L. Darrell, Francisco Chicano, and Brian W. Goldman. "Gray Box Optimization for Mk Landscapes (NK Landscapes and MAX-kSAT)." Evolutionary Computation 24, no. 3 (2016): 491–519. http://dx.doi.org/10.1162/evco_a_00184.

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This article investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in [Formula: see text] time. Bounded separable problems are also solved in [Formula: see text] time. As a result, Gray Box Optimization is able to solve many commonly used problems from the evoluti

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10

Utku, Kose, and Arslan Ahmet. "BRAIN Journal - On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 5, no. 1-4 (2014): 60–66. https://doi.org/10.5281/zenodo.1044111.

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ABSTRACT In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the sol

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Дисертації з теми "Evolutional computation"

1

Stanek, Edward Jason. "Computation of evolutionary change." [Ames, Iowa : Iowa State University], 2009.

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2

Dukkipati, Ambedkar. "ACE-Model: A Conceptual Evolutionary Model For Evolutionary Computation And Artificial Life." Thesis, Indian Institute of Science, 2002. https://etd.iisc.ac.in/handle/2005/3920.

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Darwinian Evolutionary system - a system satisfying the abstract conditions: reproduction with heritable variation, in a finite world, giving rise to Natural Selection encompasses a complex and subtle system of interrelated theories, whose substantive transplantation to any artificial medium let it be mathematical model or computational model - will be very far from easy. There are two motives in bringing Darwinian evolution into computational frameworks: one to understand the Darwinian evolution, and the other is to view Darwinian evolution - that carries out controlled adaptive-stochastic se

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3

Dukkipati, Ambedkar. "ACE-Model: A Conceptual Evolutionary Model For Evolutionary Computation And Artificial Life." Thesis, Indian Institute of Science, 2002. http://hdl.handle.net/2005/47.

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Darwinian Evolutionary system - a system satisfying the abstract conditions: reproduction with heritable variation, in a finite world, giving rise to Natural Selection encompasses a complex and subtle system of interrelated theories, whose substantive transplantation to any artificial medium let it be mathematical model or computational model - will be very far from easy. There are two motives in bringing Darwinian evolution into computational frameworks: one to understand the Darwinian evolution, and the other is to view Darwinian evolution - that carries out controlled adaptive-stochastic se

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4

Pryde, Meinwen. "Evolutionary computation and experimental design." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/evolutionary-computation-and-experimental-design(acc0a9a5-aa01-4d4a-aa4e-836ee5190a48).html.

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This thesis describes the investigations undertaken to produce a novel hybrid optimisation technique that combines both global and local searching to produce good solutions quickly. Many evolutionary computation and experimental design methods are considered before genetic algorithms and evolutionary operation are combined to produce novel optimisation algorithms. A novel piece of software is created to run two and three factor evolutionary operation experiments. A range of new hybrid small population genetic algorithms are created that contain evolutionary operation in all generations (static

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5

Schmidt, Christian. "Evolutionary computation in stochastic environments." Karlsruhe Univ.-Verl. Karlsruhe, 2007. http://www.uvka.de/univerlag/volltexte/2007/231/.

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6

Rohlfshagen, Philipp. "Molecular Algorithms for Evolutionary Computation." Thesis, University of Birmingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522032.

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7

Kaucic, Massimiliano. "Evolutionary computation for trading systems." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/3093.

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2007/2008<br>Evolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction

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8

Chellapilla, Kumar H. "Designing effective evolutionary computations /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3208643.

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9

Deugo, Dwight L. (Dwight Lorne) Carleton University Dissertation Computer Science. "Computational evolutionary epistemology." Ottawa, 1993.

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10

Yogev, Or Antonsson Erik K. Antonsson Erik K. "Computational evolutionary embryogeny /." Diss., Pasadena, Calif. : California Institute of Technology, 2009. http://resolver.caltech.edu/CaltechETD:etd-01162009-072031.

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Книги з теми "Evolutional computation"

1

Gujarathi, Ashish M., and B. V. Babu, eds. Evolutionary Computation. Apple Academic Press, 2016. http://dx.doi.org/10.1201/9781315366388.

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Gujarathi, Ashish M. Evolutionary Computation. Apple Academic Press, 2016. http://dx.doi.org/10.4324/9781315366388.

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3

1949-, Dumitrescu D., ed. Evolutionary computation. CRC Press, 2000.

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4

Fogel, David B. Evolutionary Computation. John Wiley & Sons, Ltd., 2006.

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5

Du, Zhenyu, ed. Intelligence Computation and Evolutionary Computation. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31656-2.

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6

Watanabe, Keigo, and M. M. A. Hashem. Evolutionary Computations. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39883-7.

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7

Pedrycz, Witold, ed. Fuzzy Evolutionary Computation. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6135-4.

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8

1953-, Pedrycz Witold, ed. Fuzzy evolutionary computation. Kluwer Academic Publishers, 1997.

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9

Pedrycz, Witold. Fuzzy Evolutionary Computation. Springer US, 1997.

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10

Landweber, Laura F., and Erik Winfree, eds. Evolution as Computation. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55606-7.

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Частини книг з теми "Evolutional computation"

1

Yao, Xin. "The Evolution of Evolutionary Computation." In Adaptive and Intelligent Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23857-4_4.

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2

Yao, Xin. "The Evolution of Evolutionary Computation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_6.

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3

Yao, Xin. "Evolving Computational Neural Networks Through Evolutionary Computation." In GeoComputational Modelling. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04637-1_3.

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4

Baragona, Roberto, Francesco Battaglia, and Irene Poli. "Evolutionary Computation." In Evolutionary Statistical Procedures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16218-3_2.

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5

Sanchez, Ernesto, Massimiliano Schillaci, and Giovanni Squillero. "Evolutionary computation." In Evolutionary Optimization: the µGP toolkit. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09426-7_1.

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6

Khan, Gul Muhammad. "Evolutionary Computation." In Evolution of Artificial Neural Development. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67466-7_3.

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7

Langford, John, Xinhua Zhang, Gavin Brown, et al. "Evolutionary Computation." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_272.

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8

Zhang, Xian-Da. "Evolutionary Computation." In A Matrix Algebra Approach to Artificial Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2770-8_9.

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9

Liu, Jing, Hussein A. Abbass, and Kay Chen Tan. "Evolutionary Computation." In Evolutionary Computation and Complex Networks. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60000-0_1.

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10

Tang, W. H., and Q. H. Wu. "Evolutionary Computation." In Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-052-6_2.

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Тези доповідей конференцій з теми "Evolutional computation"

1

Escobar-Cuevas, Hector, Erik Cuevas, Alberto L. Chang, et al. "Differential Evolution Search Strategy Enhancement Through Evolutionary Game Theory." In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. http://dx.doi.org/10.1109/cec60901.2024.10612179.

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2

Zhang, Yidan. "Review of evolutionary computation." In International Conference on Computer Application and Information Security (ICCAIS 2024), edited by Sadiq Ali Safaa, Pandey Hari Mohan, and Boussaid Farid. SPIE, 2025. https://doi.org/10.1117/12.3060608.

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3

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Interactive Reduct Evolutional Computation for Aesthetic Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48287.

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Анотація:

We propose a method to evolve designs based on a user’s personal preferences. The method works through an interaction between the user and a computer system. The objective of the method is to help a customer set design parameters by simple evaluation of displayed samples. An important feature of this method is that the design attributes which a user pays more attention to (favored features) are estimated with Reduct in Rough Sets Theory and are reflected while refining the design. New design candidates are generated by the user’s evaluation of design samples generated at random. While values o

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4

Yanagisawa, Hideyoshi, and Shuichi Fukuda. "Global Feature Based Interactive Reduct Evolutional Computation for Aesthetic Design." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57651.

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In this paper, we verify the enhancement of IREC, which is a computer algorithm for interactive design support system, to support global design features as design attributes. IREC (Interactive Reduct Evolutional Computation) is a method to evolve designs based on users’ personal preferences. The method works through an interaction between the user and a computer system. The computer system with IREC generates design samples consisting of random attributes and the user evaluates and scores each samples depending on his/her psychological preferences. The system estimates the design attributes th

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5

Makimura, Hidetoshi, Yuta Watanabe, Kota Watanabe, and Hajime Igarashi. "Evolutional design of small antennas for passive UHF-band RFID." In 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2010). IEEE, 2010. http://dx.doi.org/10.1109/cefc.2010.5481405.

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6

Gao, Fu-zheng, Tian-de Zhang, and Feng Chang. "An upwind finite volume element method for nonlinear evolutional problem and theory analysis." In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022522.

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7

Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570416.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274112.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 12th annual conference comp. ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830914.

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Jansen, Thomas, and Frank Neumann. "Computational complexity and evolutionary computation." In the 2008 GECCO conference companion. ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389062.

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Звіти організацій з теми "Evolutional computation"

1

Grefenstette, John. Topics in Evolutionary Computation. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada398950.

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Grefenstette, John J. Topics in Evolutionary Computation. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417080.

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Foster, James A., and Erick Cantu-Paz. The 2003 Genetic and Evolutionary Computation Conference. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada419567.

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4

Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Schiferl, S. K., and P. J. Maudlin. Evolution of plastic anisotropy for high-strain-rate computations. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10104863.

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Landis, Chad M. Computational Model for Domain Structure Evolution in Ferroelectrics. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada575644.

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Demirel, Melik Cumhar. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/821471.

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Demirel, Melik Cumhur. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/821477.

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Demirel, Melik Cumhur. Linking Experimental Characterization and Computational Modeling in Microstructural Evolution. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/821484.

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Zhou, C. FY09 Final Report for LDRD Project: Understanding Viral Quasispecies Evolution through Computation and Experiment. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/969707.

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