Bibliografie tematiche / Cartesian programming

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Letteratura scientifica selezionata sul tema "Cartesian programming"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 19 febbraio 2022

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Articoli di riviste sul tema "Cartesian programming"

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Fang, Wei, and Mindan Gu. "FMCGP: frameshift mutation cartesian genetic programming." Complex & Intelligent Systems 7, no. 3 (January 12, 2021): 1195–206. http://dx.doi.org/10.1007/s40747-020-00241-5.

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AbstractCartesian Genetic Programming (CGP) is a variant of Genetic Programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence. To address this problem in CGP, we propose a Frameshift Mutation Cartesian Genetic Programming (FMCGP), which is inspired by the DNA mutation mechanism in biology and the frameshift mutat
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Izzo, Dario, and Francesco Biscani. "dcgp: Differentiable Cartesian Genetic Programming made easy." Journal of Open Source Software 5, no. 51 (July 16, 2020): 2290. http://dx.doi.org/10.21105/joss.02290.

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Miller, Julian Francis. "Cartesian genetic programming: its status and future." Genetic Programming and Evolvable Machines 21, no. 1-2 (August 6, 2019): 129–68. http://dx.doi.org/10.1007/s10710-019-09360-6.

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Knezevic, Nikola, Branko Lukic, Kosta Jovanovic, Leon Zlajpah, and Tadej Petric. "End-effector Cartesian stiffness shaping - sequential least squares programming approach." Serbian Journal of Electrical Engineering 18, no. 1 (2021): 1–14. http://dx.doi.org/10.2298/sjee2101001k.

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Control of robot end-effector (EE) Cartesian stiffness matrix (or the whole mechanical impedance) is still a challenging open issue in physical humanrobot interaction (pHRI). This paper presents an optimization approach for shaping the robot EE Cartesian stiffness. This research targets collaborative robots with intrinsic compliance - serial elastic actuators (SEAs). Although robots with SEAs have constant joint stiffness, task redundancy (null-space) for a specific task could be used for robot reconfiguration and shaping the stiffness matrix while still keeping the EE position unchanged. The
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Yu, Zhangyi, and Sanyou Zeng. "Using Cartesian genetic programming to design wire antenna." International Journal of Computer Applications in Technology 43, no. 4 (2012): 372. http://dx.doi.org/10.1504/ijcat.2012.047163.

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Yu, Zhangyi, Sanyou Zeng, Yan Guo, and Liguo Song. "Using Cartesian genetic programming to implement function modelling." International Journal of Innovative Computing and Applications 3, no. 4 (2011): 213. http://dx.doi.org/10.1504/ijica.2011.044530.

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Drahosova, Michaela, Lukas Sekanina, and Michal Wiglasz. "Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic Programming." Evolutionary Computation 27, no. 3 (September 2019): 497–523. http://dx.doi.org/10.1162/evco_a_00229.

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In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolu
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Miller, J. F., and S. L. Smith. "Redundancy and computational efficiency in Cartesian genetic programming." IEEE Transactions on Evolutionary Computation 10, no. 2 (April 2006): 167–74. http://dx.doi.org/10.1109/tevc.2006.871253.

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Mahsal Khan, Maryam, Arbab Masood Ahmad, Gul Muhammad Khan, and Julian F. Miller. "Fast learning neural networks using Cartesian genetic programming." Neurocomputing 121 (December 2013): 274–89. http://dx.doi.org/10.1016/j.neucom.2013.04.005.

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Walker, James Alfred, Katharina Völk, Stephen L. Smith, and Julian Francis Miller. "Parallel evolution using multi-chromosome cartesian genetic programming." Genetic Programming and Evolvable Machines 10, no. 4 (October 20, 2009): 417–45. http://dx.doi.org/10.1007/s10710-009-9093-2.

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Tesi sul tema "Cartesian programming"

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Turner, Andrew. "Evolving artificial neural networks using Cartesian genetic programming." Thesis, University of York, 2015. http://etheses.whiterose.ac.uk/12035/.

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NeuroEvolution is the application of Evolutionary Algorithms to the training of Artificial Neural Networks. NeuroEvolution is thought to possess many benefits over traditional training methods including: the ability to train recurrent network structures, the capability to adapt network topology, being able to create heterogeneous networks of arbitrary transfer functions, and allowing application to reinforcement as well as supervised learning tasks. This thesis presents a series of rigorous empirical investigations into many of these perceived advantages of NeuroEvolution. In this work it is d
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Walker, James Alfred. "The automatic acquisition, evolution and re-use of modules in cartesian genetic programming." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444766.

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Kečkéš, Miroslav. "Automatizovaný návrh obrazových filtrů na základě kartézského genetického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219455.

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The aim of this diploma thesis is using cartesian genetic programming on design image filters and creating basic structure for implement diferent type of problems. Genetic programming is rapidly growing method, which often using for solve dificult problems. This thesis analyze basic principle, way of application and implementing this method to design filters. Result of this thesis is program realize design filters define by specific parameters, overview of implementig method and achieve summary from this sphere.
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Cattani, Philip Thomas. "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification." Thesis, University of Kent, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651.

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Abstract (sommario):
Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin's theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, name
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Minařík, Miloš. "Sebemodifikující se programy v kartézském genetickém programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237114.

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During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make its use in area of large and generic problems impossible. These limitations can be eliminated using the recent method allowing self-modification of programs in cartesian genetic programming. The purpose of this thesis is to review the development in this area done so far. Next objective is to design own solutions for solving various problems that are hardly solvable using the ordinary cartesian genetic programming. One of the problem
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Oliveira, Marcelo Frasson de. "Projeto de um robô cartesiano com acionamento pneumático." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/13444.

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A grande maioria dos robôs industriais disponíveis no mercado é de alto desempenho, principalmente com relação à precisão de posicionamento. Este aspecto é um dos fatores que mais influencia no seu preço final, levando em consideração toda a complexa cadeia de elementos que fazem com que o robô opere corretamente, desde os motores e componentes mecânicos, passando pela arquitetura e sistemas de controle até o sistema de programação. Tendo isto em vista, este trabalho visa projetar um robô industrial com preço mais acessível, adequado para o uso em processos industriais que não necessitem altos
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Vaňák, Tomáš. "Využití regresních metod pro predikci dopravy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236096.

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Master thesis deals with possibilities of predicting traffic situation on the macroscopic level using data, that were recorded using traffic sensors. This sensors could be loop detectors, radar detectors or cameras. The main problem discussed in this thesis is the travel time of cars. A method for travel time prediction was designed and implemented as a part of this thesis. Data from real traffic were used to test the designed method. The first objective of this thesis is to become familiar with the prediction methods that will be used. The main objective is to use the acquired knowledge to de
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Klemšová, Jarmila. "Modularita v evolučním návrhu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236989.

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The diploma thesis deals with the evolutionary algorithms and their application in the area of digital circuit design. In the first part, general principles of evolutionary algorithms are introduced. This part includes also the introduction of genetic algorithms and genetic programming. The next chapter describes the cartesian genetic programming and its modifications like embedded, self-modifying or multi-chromosome cartessian genetic programming. Essential part of this work consists of the design and implementation of a modularization technique for evolution circuit design. The proposed appr
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Vácha, Petr. "Křížení v kartézském genetickém programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-235481.

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Optimization of digital circuits still attracts much attention not only of researchers but mainly chip producers. One of new the methods for the optimization of digital circuits is cartesian genetic programming. This Master's thesis describes a new crossover operator and its implementation for cartesian genetic programming. Experimental evaluation was performed in the task of three-bit multiplier and five-bit parity circuit design.
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Husa, Jakub. "Genetické vylepšení software pro kartézské genetické programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255458.

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Genetic programming is a nature-inspired method of programming that allows an automated creation and adaptation of programs. For nearly two decades, this method has been able to provide human-comparable results across many fields. This work gives an introduction to the problems of evolutionary algorithms, genetic programming and the way they can be used to improve already existing software. This work then proposes a program able to use these methods to improve an implementation of cartesian genetic programming (CGP). This program is then tested on a CGP implementation created specifically for
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Libri sul tema "Cartesian programming"

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service), SpringerLink (Online, ed. Cartesian Genetic Programming. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Miller, Julian F., ed. Cartesian Genetic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3.

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Miller, Julian F. Cartesian Genetic Programming. Springer, 2013.

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Miller, Julian F. Cartesian Genetic Programming. Springer, 2011.

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Center, NASA Glenn Research, ed. An automated code generator for three-dimensional acoustic wave propagation with geometrically complex solid wall boundaries. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 1999.

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Capitoli di libri sul tema "Cartesian programming"

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Miller, Julian F. "Cartesian Genetic Programming." In Cartesian Genetic Programming, 17–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_2.

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Miller, Julian F., and Peter Thomson. "Cartesian Genetic Programming." In Lecture Notes in Computer Science, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-46239-2_9.

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DiPaola, Steve, and Nathan Sorenson. "CGP, Creativity and Art." In Cartesian Genetic Programming, 293–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_10.

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Sekanina, Lukas, Simon L. Harding, Wolfgang Banzhaf, and Taras Kowaliw. "Image Processing and CGP." In Cartesian Genetic Programming, 181–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_6.

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Harding, Simon L., and Wolfgang Banzhaf. "Hardware Acceleration for CGP: Graphics Processing Units." In Cartesian Genetic Programming, 231–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_8.

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Khan, Gul Muhammad, and Julian F. Miller. "The CGP Developmental Network." In Cartesian Genetic Programming, 255–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_9.

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Miller, Julian F. "Introduction to Evolutionary Computation and Genetic Programming." In Cartesian Genetic Programming, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_1.

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Smith, Stephen L., James Alfred Walker, and Julian F. Miller. "Medical Applications of Cartesian Genetic Programming." In Cartesian Genetic Programming, 309–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_11.

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Walker, James Alfred, Julian F. Miller, Paul Kaufmann, and Marco Platzner. "Problem Decomposition in Cartesian Genetic Programming." In Cartesian Genetic Programming, 35–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_3.

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Harding, Simon L., Julian F. Miller, and Wolfgang Banzhaf. "Self-Modifying Cartesian Genetic Programming." In Cartesian Genetic Programming, 101–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_4.

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Atti di convegni sul tema "Cartesian programming"

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Miller, Julian Francis, and Simon L. Harding. "Cartesian genetic programming." In the 2008 GECCO conference companion. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1388969.1389075.

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Miller, Julian Francis, and Simon L. Harding. "Cartesian genetic programming." In the 11th annual conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570428.

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Hara, Akira, Manabu Watanabe, and Tetsuyuki Takahama. "Cartesian Ant Programming." In 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2011. http://dx.doi.org/10.1109/icsmc.2011.6084146.

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Miller, Julian F., and Simon L. Harding. "Cartesian genetic programming." In the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830924.

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Miller, Julian, and Andrew Turner. "Cartesian Genetic Programming." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739482.2756571.

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Plaice, John, and Blanca Mancilla. "Cartesian Programming: The TransLucid Programming Language." In 2009 33rd Annual IEEE International Computer Software and Applications Conference. IEEE, 2009. http://dx.doi.org/10.1109/compsac.2009.139.

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Harding, Simon, Julian F. Miller, and Wolfgang Banzhaf. "Self modifying cartesian genetic programming." In the 12th annual conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830483.1830591.

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Harding, Simon L., Julian F. Miller, and Wolfgang Banzhaf. "Self-modifying cartesian genetic programming." In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277161.

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Harter, Adam, Daniel R. Tauritz, and William M. Siever. "Asynchronous parallel cartesian genetic programming." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3084210.

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Hrbacek, Radek, and Michaela Sikulova. "Coevolutionary Cartesian Genetic Programming in FPGA." In European Conference on Artificial Life 2013. MIT Press, 2013. http://dx.doi.org/10.7551/978-0-262-31709-2-ch062.

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