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Articoli di riviste sul tema "Cartesian programming"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 19 febbraio 2022

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1

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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5

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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6

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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10

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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Harding, Simon, Julian F. Miller, and Wolfgang Banzhaf. "Developments in Cartesian Genetic Programming: self-modifying CGP." Genetic Programming and Evolvable Machines 11, no. 3-4 (June 25, 2010): 397–439. http://dx.doi.org/10.1007/s10710-010-9114-1.

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12

Turner, Andrew James, and Julian Francis Miller. "Recurrent Cartesian Genetic Programming of Artificial Neural Networks." Genetic Programming and Evolvable Machines 18, no. 2 (August 8, 2016): 185–212. http://dx.doi.org/10.1007/s10710-016-9276-6.

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13

Paris, P. C. D., E. C. Pedrino, and M. C. Nicoletti. "Automatic learning of image filters using Cartesian genetic programming." Integrated Computer-Aided Engineering 22, no. 2 (February 1, 2015): 135–51. http://dx.doi.org/10.3233/ica-150482.

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14

Balandina, G. I. "Control System Synthesis by Means of Cartesian Genetic Programming." Procedia Computer Science 103 (2017): 176–82. http://dx.doi.org/10.1016/j.procs.2017.01.051.

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15

Smith, Stephen. "Cartesian Genetic Programming and its Application to Medical Diagnosis." IEEE Computational Intelligence Magazine 6, no. 4 (November 2011): 56–67. http://dx.doi.org/10.1109/mci.2011.942583.

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16

Kadlic, Branislav, Ivan Sekaj, and Daniel Pernecký. "Design of Continuous-time Controllers using Cartesian Genetic Programming." IFAC Proceedings Volumes 47, no. 3 (2014): 6982–87. http://dx.doi.org/10.3182/20140824-6-za-1003.00915.

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17

Manazir, Abdul, and Khalid Raza. "Recent Developments in Cartesian Genetic Programming and its Variants." ACM Computing Surveys 51, no. 6 (February 27, 2019): 1–29. http://dx.doi.org/10.1145/3275518.

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18

Turner, Andrew James, and Julian Francis Miller. "Neutral genetic drift: an investigation using Cartesian Genetic Programming." Genetic Programming and Evolvable Machines 16, no. 4 (May 6, 2015): 531–58. http://dx.doi.org/10.1007/s10710-015-9244-6.

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19

Narendran, Paliath, Frank Pfenning, and Richard Statman. "On the unification problem for Cartesian closed categories." Journal of Symbolic Logic 62, no. 2 (June 1997): 636–47. http://dx.doi.org/10.2307/2275552.

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AbstractCartesian closed categories (CCCs) have played and continue to play an important role in the study of the semantics of programming languages. An axiomatization of the isomorphisms which hold in all Cartesian closed categories discovered independently by Soloviev and Bruce, Di Cosmo and Longo leads to seven equalities. We show that the unification problem for this theory is undecidable, thus settling an open question. We also show that an important subcase, namely unification modulo thelinear isomorphisms, is NP-complete. Furthermore, the problem of matching in CCCs is NP-complete when

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20

Yazdani, Samaneh, Jamshid Shanbehzadeh, and Esmaeil Hadavandi. "MBCGP-FE: A modified balanced cartesian genetic programming feature extractor." Knowledge-Based Systems 135 (November 2017): 89–98. http://dx.doi.org/10.1016/j.knosys.2017.08.005.

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21

Kazarlis, S. A., J. Kalomiros, and V. Kalaitzis. "A Cartesian Genetic Programming Approach for evolving Optimal Digital Circuits." Journal of Engineering Science and Technology Review 9, no. 5 (October 2016): 88–92. http://dx.doi.org/10.25103/jestr.095.13.

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22

Suganuma, Masanori, Masayuki Kobayashi, Shinichi Shirakawa, and Tomoharu Nagao. "Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming." Evolutionary Computation 28, no. 1 (March 2020): 141–63. http://dx.doi.org/10.1162/evco_a_00253.

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The convolutional neural network (CNN), one of the deep learning models, has demonstrated outstanding performance in a variety of computer vision tasks. However, as the network architectures become deeper and more complex, designing CNN architectures requires more expert knowledge and trial and error. In this article, we attempt to automatically construct high-performing CNN architectures for a given task. Our method uses Cartesian genetic programming (CGP) to encode the CNN architectures, adopting highly functional modules such as a convolutional block and tensor concatenation, as the node fu

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23

Fuchuan, N. I., L. I. Yuanxiang, and K. E. Peng. "ONMCGP: Orthogonal Neighbourhood Mutation Cartesian Genetic Programming for Evolvable Hardware." Journal of Physics: Conference Series 490 (March 11, 2014): 012194. http://dx.doi.org/10.1088/1742-6596/490/1/012194.

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Turner, Andrew James, and Julian Francis Miller. "Introducing a cross platform open source Cartesian Genetic Programming library." Genetic Programming and Evolvable Machines 16, no. 1 (August 31, 2014): 83–91. http://dx.doi.org/10.1007/s10710-014-9233-1.

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25

Plaice, John, Blanca Mancilla, and Gabriel Ditu. "From Lucid to TransLucid: Iteration, Dataflow, Intensional and Cartesian Programming." Mathematics in Computer Science 2, no. 1 (November 2008): 37–61. http://dx.doi.org/10.1007/s11786-008-0043-9.

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26

OLTEAN, MIHAI, CRINA GROŞAN, LAURA DIOŞAN, and CRISTINA MIHĂILĂ. "GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 197–238. http://dx.doi.org/10.1142/s0218213009000111.

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Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Inte

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27

Henglein, Fritz, and Ken Friis Larsen. "Generic multiset programming with discrimination-based joins and symbolic Cartesian products." Higher-Order and Symbolic Computation 23, no. 3 (September 2010): 337–70. http://dx.doi.org/10.1007/s10990-011-9078-8.

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BEN-ASHER, YOSI. "THE CARTESIAN PRODUCT PROBLEM AND IMPLEMENTING PRODUCTION SYSTEMS ON RECONFIGURABLE MESHES." Parallel Processing Letters 05, no. 01 (March 1995): 49–61. http://dx.doi.org/10.1142/s0129626495000060.

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Let A and B be two groups of up to n elements distributed on the first row of an n × n reconfigurable mesh, and CA,B a subset of the cartesian product A × B satisfying some unknown condition C. Only one broadcasting step is needed in order to compute CA,B's elements. However, the problem of moving CA,B's elements to the first row in optimal time (so that they can be further processed) is not trivial. The conditional cartesian product (CCP) problem is to move CA,B's elements to the first row in [Formula: see text] steps. This requires optimizing the cartesian product operation such that CA,B's

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29

Wang, Yang, Tian Huang, and Clement M. Gosselin. "Interpolation Error Prediction of a Three-Degree Parallel Kinematic Machine." Journal of Mechanical Design 126, no. 5 (September 1, 2004): 932–37. http://dx.doi.org/10.1115/1.1767184.

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In this paper, an NC interpolation algorithm for a tripod-based parallel kinematic machine is investigated. The algorithm can be implemented in two steps, the rough interpolation in the Cartesian space and the precise interpolation in the actuator space. The upper bound of the theoretical interpolation error due to the interpolation algorithm in the precise interpolation and nonlinear mapping is analyzed. The representation of the interpolation error distribution within the Cartesian space is depicted in terms of the variations of the interpolation period and the programming velocity. It was c

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30

Spreen, Dieter. "On domains witnessing increase in information." Applied General Topology 1, no. 1 (October 1, 2000): 129. http://dx.doi.org/10.4995/agt.2000.13640.

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<p>The paper considers algebraic directed-complete partial orders with a semi-regular Scott topology, called regular domains. As is well know, the category of Scott domains and continuous maps is Cartesian closed. This is no longer true, if the domains are required to be regular. Two Cartesian closed subcategories of the regular Scott domains are exhibited: regular dI-domains with stable maps and strongly regular Scott domains with continuous maps. Here a Scott domains is strongly regular if all of its compact open subsets are regular open. In one considers only embeddings of dependent p

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31

Khan, Maryam Mahsal, Alexandre Mendes, Ping Zhang, and Stephan K. Chalup. "Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming." Neurocomputing 247 (July 2017): 39–58. http://dx.doi.org/10.1016/j.neucom.2017.03.048.

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32

Walker, J. A., and J. F. Miller. "The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming." IEEE Transactions on Evolutionary Computation 12, no. 4 (August 2008): 397–417. http://dx.doi.org/10.1109/tevc.2007.903549.

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Dzalbs, Ivars, and Tatiana Kalganova. "Forecasting Price Movements in Betting Exchanges Using Cartesian Genetic Programming and ANN." Big Data Research 14 (December 2018): 112–20. http://dx.doi.org/10.1016/j.bdr.2018.10.001.

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34

Alvarado-Velazco, Paola B., Victor Ayala-Ramirez, and Raul E. Sanchez-Yanez. "Polygonal Approximation of Digital Curves Using Evolutionary Programming." Acta Universitaria 22 (March 1, 2012): 15–20. http://dx.doi.org/10.15174/au.2012.336.

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This paper proposes an Evolutionary Programming (EP) approach to solve the polygonal approximation of digital curves. The solution provided by the method consists of a sequence of straight line segments to be applied as Advance and Rotate motion primitives of a 2D Cartesian robot. The proposed approach finds automatically the number of segments and the startingand ending points of each of them. We have tested our approach on a test set of digital curves that exhibits two main qualitative features: openess and straightness, in different degrees. We show that our method obtains good results for

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35

Lee, Hyung Joo, and Sigrid Brell-Cokcan. "Cartesian coordinate control for teleoperated construction machines." Construction Robotics 5, no. 1 (February 22, 2021): 1–11. http://dx.doi.org/10.1007/s41693-021-00055-y.

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AbstractDespite the continuous development of the hardware, most construction machines are exclusively teleoperated limiting the control to a single paradigm. The operators usually have to move different joints of the machine in a coordinated way solely relied on their experiences leading to reduced local accuracy and work efficiency. Automation of construction machinery can open up new possibilities to improve efficiency and safety during the construction process. This work introduces a generic method that can adapt construction machines that have been already used in the field for decades, s

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Walker, James Alfred, Yang Liu, Gianluca Tempesti, Jon Timmis, and Andy M. Tyrrell. "Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming." International Journal of Adaptive, Resilient and Autonomic Systems 3, no. 4 (October 2012): 32–50. http://dx.doi.org/10.4018/jaras.2012100103.

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Transport triggered architectures are used for implementing bio-inspired systems due to their simplicity, modularity and fault-tolerance. However, producing efficient, optimised machine code for such architectures is extremely difficult, since computational complexity has moved from the hardware-level to the software-level. Presented is the application of Cartesian Genetic Programming (CGP) to the evolution of machine code for a simple implementation of transport triggered architecture. The effectiveness of the algorithm is demonstrated by evolving machine code for a 4-bit multiplier with thre

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Ullah, Qazi Zia, Gul Muhammad Khan, and Shahzad Hassan. "Cloud Infrastructure Estimation and Auto-Scaling Using Recurrent Cartesian Genetic Programming-Based ANN." IEEE Access 8 (2020): 17965–85. http://dx.doi.org/10.1109/access.2020.2966678.

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38

Miragaia, Rolando, Francisco Fernández, Gustavo Reis, and Tiago Inácio. "Evolving a Multi-Classifier System for Multi-Pitch Estimation of Piano Music and Beyond: An Application of Cartesian Genetic Programming." Applied Sciences 11, no. 7 (March 24, 2021): 2902. http://dx.doi.org/10.3390/app11072902.

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This paper presents a new method with a set of desirable properties for multi-pitch estimation of piano recordings. We propose a framework based on a set of classifiers to analyze audio input and to identify piano notes present in a given audio signal. Our system’s classifiers are evolved using Cartesian genetic programming: we take advantage of Cartesian genetic programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Two significant improvements are described: the use of a harmonic mask for better fitness values and a data augmentation proces

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Khan, Gul Muhammad, Julian F. Miller, and David M. Halliday. "Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture." Evolutionary Computation 19, no. 3 (September 2011): 469–523. http://dx.doi.org/10.1162/evco_a_00043.

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Although artificial neural networks have taken their inspiration from natural neurological systems, they have largely ignored the genetic basis of neural functions. Indeed, evolutionary approaches have mainly assumed that neural learning is associated with the adjustment of synaptic weights. The goal of this paper is to use evolutionary approaches to find suitable computational functions that are analogous to natural sub-components of biological neurons and demonstrate that intelligent behavior can be produced as a result of this additional biological plausibility. Our model allows neurons, de

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Ullah, Qazi Zia, Gul Muhammad Khan, Shahzad Hassan, Asif Iqbal, Farman Ullah, and Kyung Sup Kwak. "A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction." Electronics 10, no. 1 (January 1, 2021): 67. http://dx.doi.org/10.3390/electronics10010067.

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Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by wi

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Yazdani, Samaneh, and Jamshid Shanbehzadeh. "Balanced Cartesian Genetic Programming via migration and opposition-based learning: application to symbolic regression." Genetic Programming and Evolvable Machines 16, no. 2 (July 29, 2014): 133–50. http://dx.doi.org/10.1007/s10710-014-9230-4.

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Ceccarelli, Marco, Francisco Valero, Vicente Mata, and Ignacio Cuadrado†. "Generation of adjacent configurations for a collision-free path planning of manipulators." Robotica 14, no. 4 (July 1996): 391–96. http://dx.doi.org/10.1017/s0263574700019780.

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SUMMARYIn this paper an algorithm is proposed for the problem of path planning of redundant manipulators among obstacles by using a suitable formulation for robot configurations and path strategy. In particular robotic manipulators have been modelled by using reference points on the kinematic chain and their Cartesian coordinates description. The path planning has been formulated as an optimization problem for the determination of adjacent configurations and the path among obstacles with minimum manipulator displacement. The fully Cartesian coordinates description has been useful for the econo

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43

Shmalko, Elizaveta, and Askhat Diveev. "Control Synthesis as Machine Learning Control by Symbolic Regression Methods." Applied Sciences 11, no. 12 (June 12, 2021): 5468. http://dx.doi.org/10.3390/app11125468.

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The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computati

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Dzhenzher, V. O., and L. V. Denisova. "SCIENTIFIC GRAPHICS IN PASCALABC.NET: PLOTTING FUNCTION GRAPHS IN A RECTANGULAR CARTESIAN COORDINATE SYSTEM." Informatics in school, no. 1 (March 11, 2020): 31–39. http://dx.doi.org/10.32517/2221-1993-2020-19-1-31-39.

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The article considers the algorithm for plotting functions in a rectangular Cartesian coordinate system. A method is described for converting coordinates from a natural system to a screen system. The algorithm is implemented in the programming language PascalABC.NET. The necessary information about the graphical tools of the language is presented, the program code with detailed explanations is given. A distinctive feature of the method is the use of visual and easily calculated parameters for scaling the graph.

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Parziale, A., R. Senatore, A. Della Cioppa, and A. Marcelli. "Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues." Artificial Intelligence in Medicine 111 (January 2021): 101984. http://dx.doi.org/10.1016/j.artmed.2020.101984.

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Mora, Javier, Rubén Salvador, and Eduardo de la Torre. "On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming." Genetic Programming and Evolvable Machines 20, no. 2 (October 1, 2018): 155–86. http://dx.doi.org/10.1007/s10710-018-9340-5.

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Elola, Andoni, Javier Del Ser, Miren Nekane Bilbao, Cristina Perfecto, Enrique Alexandre, and Sancho Salcedo-Sanz. "Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems." Applied Soft Computing 52 (March 2017): 760–70. http://dx.doi.org/10.1016/j.asoc.2016.09.049.

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Poli, Riccardo, and Nicholas Freitag McPhee. "General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part I." Evolutionary Computation 11, no. 1 (March 2003): 53–66. http://dx.doi.org/10.1162/106365603321829005.

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This is the first part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover. The theory is based on a Cartesian node reference system which makes it possible to describe programs as functions over the space N2 and allows one to model the process of selection of the crossover points of subtree-swapping crossovers as a probability distribution over N4. In Part I, we present these notions and models and show how they can be used to calculate useful quantities. In Part II we will show how this machinery, when integrated with othe

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MANZONETTO, GIULIO. "What is a categorical model of the differential and the resource λ-calculi?" Mathematical Structures in Computer Science 22, № 3 (27 лютого 2012): 451–520. http://dx.doi.org/10.1017/s0960129511000594.

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The differential λ-calculus is a paradigmatic functional programming language endowed with a syntactical differentiation operator that allows the application of a program to an argument in a linear way. One of the main features of this language is that it is resource conscious and gives the programmer suitable primitives to handle explicitly the resources used by a program during its execution. The differential operator also allows us to write the full Taylor expansion of a program. Through this expansion, every program can be decomposed into an infinite sum (representing non-deterministic cho

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Dourado, Antonio Miguel Batista, and Emerson Carlos Pedrino. "Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired People." Applied Soft Computing 89 (April 2020): 106130. http://dx.doi.org/10.1016/j.asoc.2020.106130.

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