Relevant bibliographies by topics / Cartesian programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Cartesian programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "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 mutation caused by insertion or deletion of nodes is introduced to CGP. The individual in FMCGP has variable-length genotype and the proposed frameshift mutation operator helps to generate more diverse offspring individuals by changing the compiling framework of genotype. FMCGP is evaluated on the symbolic regression problems and Even-parity problems. Experimental results show that FMCGP does not exhibit the bloat problem and the use of frameshift mutation improves the search performance of the standard CGP.
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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 method proposed in this paper to investigate null-space reconfiguration's influence on Cartesian robot stiffness is based on the Sequential Least Squares Programming (SLSQP) algorithm, which presents an expansion of the quadratic programming algorithm for nonlinear functions with constraints. The method is tested in simulations for 4 DOF planar robot. Results are presented for control of the EE Cartesian stiffness initially along one axis, and then control of stiffness along both planar axis - shaping the main diagonal of the EE stiffness matrix.
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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 evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.
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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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Dissertations / Theses on the topic "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 demonstrated that the ability to simultaneously adapt network topology along with connection weights represents a significant advantage of many NeuroEvolutionary methods. It is also demonstrated that the ability to create heterogeneous networks comprising a range of transfer functions represents a further significant advantage. This thesis also investigates many potential benefits and drawbacks of NeuroEvolution which have been largely overlooked in the literature. This includes the presence and role of genetic redundancy in NeuroEvolution's search and whether program bloat is a limitation. The investigations presented focus on the use of a recently developed NeuroEvolution method based on Cartesian Genetic Programming. This thesis extends Cartesian Genetic Programming such that it can represent recurrent program structures allowing for the creation of recurrent Artificial Neural Networks. Using this newly developed extension, Recurrent Cartesian Genetic Programming, and its application to Artificial Neural Networks, are demonstrated to be extremely competitive in the domain of series forecasting.
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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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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, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called 'Cartesian', because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency - the time it takes to solve a problem - of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.
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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 problems to be considered is generating the terms of various Taylor series. Due to the fact that the solution to this problem requires generalisation, the goal is to prove that the self-modifying cartesian genetic programming scores better than classic one for this problem. Another discussed problem is using the self-modifying genetic programming for developing arbitrarily large sorting networks. In this case, the objective is to prove that self-modification brings new features to the cartesian genetic programming allowing the development of arbitrarily sized designs.
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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 níveis de precisão. Para a redução de custos de fabricação e de componentes do robô, este trabalho viabiliza o uso de atuadores pneumáticos lineares como fonte motriz, pois os mesmos são relativamente baratos, leves, não poluentes, de fácil montagem e operação, além de apresentarem uma boa relação peso/potência. Para tanto, foi implementado uma estratégia de controle por modos deslizantes com objetivo de superar as dificuldades impostas pelo comportamento não-linear dos componentes pneumáticos. Com relação à redução de custos de programação e operação do robô, desenvolveu-se um ambiente de programação off-line, através de softwares de auxilio à manufatura e de engenharia usualmente encontrados em ambientes industriais. A estratégia fundamental neste trabalho, foi o desenvolvimento de uma metodologia de projeto própria, concebida especificamente para a aplicação em projetos de robôs industriais, com os atributos de facilidade de execução e modularidade das fases envolvidas. A qual, no presente trabalho, apresenta o desenvolvimento de um robô cartesiano com três graus de liberdade acionado por atuadores pneumáticos lineares.
The great majority of industrial robots available in the market have high performance, especially relative to position accuracy. This aspect is one of the factors that most influence its final price, taking into account all complicated web elements that makes the robot operates in the correct form, since the actuators and the constructive part, passing by the architecture and control systems until the system of programming. According to these, the present work aims to project an industrial robot with more accessible costs, adequate to use in industrial process that not require high level of accuracy. For the reduction of manufacture and components costs of the robot, this work make viable to use of pneumatic actuators like a motive source, because are relatively cheap, light, not pollutants, easy assembly and operation, besides presenting a good relation weight/power. For such purpose, the strategy of control was implemented by sliding mode control for the objective to surpass the difficulties imposed by the non-linear behavior of the pneumatic components. About the reduction of programming and operation costs of the robot, an off-line programming environment was developed through manufacturing aided software and a software of engineering both usually found in industrial environments. The basic strategy in this work, was the development of an own methodology of project, conceived specifically for the application in projects of industrial robots, with the attributes of easiness of execution and modularization of the wrapped phases. That methodology, in the present work, presents the development of a Cartesian robot with three degrees of freedom actuated by pneumatic servo drive.
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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 design and to implement an aplication that will predict required traffic variables.
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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 approach is evaluated using a set of standard benchmark circuits.
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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 this project, and its functionality is then verified on other already existing implementations of CGP.
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Books on the topic "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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Book chapters on the topic "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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Conference papers on the topic "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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