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About 10,000 papers for “SIMPLE algorithm”
Rafael Lahoz-Beltrá · Figshare · 2016
Simple Genetic Algorithm (SGA) than can be used for the purposes of education and research. SGA is used in a simple optimization problem: Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) <br>has a maximum value at x=11 (binary value is equal to 1011).
Cited by 1Open access
Fantuzzi, Nicholas · AMS Campus - AlmaDL · 2017
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Yasmina Abdeddaïm, Eugène Asarin, Mihaela Sighireanu · HAL (Le Centre pour la Communication Scientifique Directe) · 2009
Abstract. We propose a subclass of timed game automata (TGA), called Task TGA, representing networks of communicating tasks where the system can choose when to start the task and the environment can choose the duration of the task. We search to solve finite-horizon reachability games on Task TGA by building strategies in the form of Simple Temporal Networks with Uncertainty (STNU). Such strategies have the advantage of being very succinct due to the partial order reduction of independent tasks. We show that the existence of such strategies is an NP-complete problem. A practical consequence of this result is a fully forward algorithm for building STNU strategies. Potential applications of this work are planning and scheduling under temporal uncertainty. 1
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Lyle Cook, James R. McWilliam · The Two-Year College Mathematics Journal · 1983
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S. V. N. Vishwanathan, M. Narasimha Murty · 2002
Abstract- We present a fast iterative algorithm for identifying the Support Vectors of a given set of points. Our algorithm works by maintaining a candidate Support Vector set. It uses a greedy approach to pick points for inclusion in the candidate set. When the addition of a point to the candidate set is blocked because of other points already present in the set we use a backtracking approach to prune away such points. To speed up convergence we initialize our algorithm with the nearest pair of points from opposite classes. We then use an optimization based approach to increment or prune the candidate Support Vector set. The al-gorithm makes repeated passes over the data to satisfy the KKT constraints. The memory requirements of our algorithm scale as O(|S|2) in the average case, where |S | is the size of the Support Vector set. We show that the algorithm is extremely competitive as compared to other conventional iterative algorithms like SMO and the NPA. We present results on a variety of real life datasets to validate our claims. I.
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Rob Smith, Johnt Prince · 1997
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Lahoz-Beltra, Rafael · Figshare · 2016
Simple Genetic Algorithm (SGA) is a 'vanilla' GA that can be used for the purposes of education and research. SGA is applied in a simple optimization problem: <br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).
Cited by 1Open access
Lahoz-Beltra, Rafael · Figshare · 2016
Simple Genetic Algorithm (SGA) is a 'vanilla' GA that can be used for the purposes of education and research. SGA is applied in a simple optimization problem: <br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).
Cited by 1Open access
Lahoz-Beltra Rafael · Figshare · 2016
Simple Genetic Algorithm (SGA) is a 'vanilla' GA that can be used for the purposes of education and research. SGA is applied in a simple optimization problem: <br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).
Cited by 4Open access
Алексей Русин · Journal of Project Management · 2025
The basic principles, issues, and methods of planning projects work for developing and implementing integrated accounting systems that help with projects of varying complexity and scale have been described. A simple planning algorithm has been considered, which is applicable in most similar projects. The importance of setting goals and assigning tasks has been emphasized, and a review of planning and plans types has been conducted. Methods for assessing the complexity of tasks in relation to projects for developing and implementing integrated accounting systems have been proposed. It has been emphasized that competent planning is accompanied by working with risks, resources, and time, which contributes to the timely and effective plan implementation.
Cited by 0Open access
David Y. Hsu · The Two-Year College Mathematics Journal · 1982
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W. H. J. Feijen, A. J. M. van Gasteren · Texts and monographs in computer science · 1999
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Feng Gao (3548), Yanfeng Li (113424), Zhihe Su (20284839) et al. · Figshare · 2024
<div><p>Human breathing is crucial for studying indoor environments and human health. Computational Fluid Dynamics (CFD) is a key tool for simulating human respiration. To enhance the accuracy of CFD simulations and reduce computation time, a new simulation strategy for human respiration is proposed in this paper. The effects of steady versus unsteady boundary conditions on simulation results were examined. For the unsteady boundary, sinusoidal exhalation velocities and non-inhalation gas were assumed, while the steady boundary involved constant velocities during both exhalation and inhalation phases. The jet center trajectory under different boundary conditions was analyzed and compared with experimental data. Additionally, variations in pollutant dispersion near the mouth under the two boundary conditions were discussed. Furthermore, the paper compared the calculation accuracy, calculation time and memory occupied by a single turbulence model or switching flow character models in human respiration simulation. Differences in exhaled gas vorticity and jet penetration depth across different flow models were identified. Finally, combined with the non-iterative algorithm, the optimal strategy of human respiration simulation was proposed. Results show that under the comprehensive consideration of calculation accuracy, calculation time and memory occupancy, using sinusoidal expiratory boundary conditions combined with the PISO algorithm, with the RNG <i>k-ε</i> model during expiratory phase, and switching into the laminar flow during inspiratory phase, is the optimal strategy of simulating human breathing.</p></div>
Cited by 0Open access
Jayadev Misra Department, Jayadev Misra · 1989
this paper deals with a simpler problem---the number of reliable processes exceeds thrice the number of unreliable processes---which has a simpler algorithm
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Maqbool, Fahad · Zenodo (CERN European Organization for Nuclear Research) · 2022
First release of FAIR genetic algorithm following FAIR guidelines for algorithms.
Cited by 0Open access
M.A. Shokrollahi, V. Stemann · 1996
We describe a very simple and efficient new algorithm for the approximation of complex numbers by algebraic integers in Z[e 2i=8 ] whose coeffcients with respect to the usual basis are bounded in absolute value by a given integer M . Its main idea is the use of a novel signature technique. An important application is the reduction of dynamic range requirements for residue number system implementations of the discrete Fourier transform. The algorithm uses at most 10 log(M) arithmetic steps and 2:4 log(M) additional memroy. It yields approximations within a distance of at most 3:42=M . Several examples are included which show that the algorithm is very fast in practice. For instance, 50000 complex approximations take less than 0.7 seconds on a SPARC-5. ii 1 Introduction In a pioneering paper Cozzens and Finkelstein [1] suggest to use cyclotomic integers in the ring Z[i] := n ff 0 + ff 1 i + : : : + ff 2 n\\Gamma1 \\Gamma1 i 2 n\\Gamma1 \\Gamma1 fi fi fi ff i 2 Z o , i = e 2i=2...
Cited by 0
Jian Qin, Huachen Pan, M. M. Rahman et al. · Mathematics and Computers in Simulation · 2020
A comparative assessment between SIMPLE and its variant SIMPLE-C is conducted based on two-dimensional (2-D) incompressible flows, using a cell-centered finite-volume Δ-formulation on a collocated grid. The SIMPLE-C (SIMPLE Compressibility) scheme additionally combines the concept of artificial compressibility (AC) with the pressure Poisson equation, provoking the diagonal dominance of influence coefficients. An improved nonlinear momentum interpolation scheme is employed at the cell face in discretizing the continuity equation to suppress pressure oscillations. The pseudo-time step Δti remains the same in both schemes to conserve an analogous scaling with momentum and scalar nodal influence coefficients. Numerical experiments in reference to buoyancy-driven cavity flow dictate that both contrivances execute a residual smoothing enhancement, facilitating an avoidance of the velocity/pressure under-relaxation (UR). However, compared with the SIMPLE approach, included benefits of the SIMPLE-C method are the use of larger Courant numbers, enhanced robustness and convergence. Excellent consistency is obtained between results available in the literature and numerical solutions obtained by both SIMPLE and SIMPLE-C solvers. The segregated SIMPLE algorithm is finally reformulated in conjunction with a new slope/flux limiter function to predict fluid flow at all speeds. Numerical results show that the compressible variant of SIMPLE replicates correct shock speed, well-resolved shock front, contact discontinuity and rarefaction waves when compared with analytical solutions. Compressible laminar flows are computed to further support the accuracy and robustness of proposed algorithm.
Cited by 19Open access
Horst Samulowitz, Chandra Reddy, Ashish Sabharwal et al. · Lecture notes in computer science · 2013
Cited by 14
R Wafdan, Mahyus Ihsan, Rahmah Zuhra · SHILAP Revista de lepidopterología · 2014
This paper discusses about connectivity algorithm in simple graphs. The algorithm has three stages, namely, adjacent vertices search, adjacent vertices investigation and component investigation. The aim of connectivity algorithm is to check whether a graph is connected or disconnected with the scope set. There are three sets which are used in this algorithm, namely, vertex, edge and adjacent vertices sets. The simple graph is connected if the graph has only one component or if the number of adjacent vertices elements is equal to the number of vertices. Otherwise, if the simple graph has more than one component or if the number of adjacent vertices elements is not equal to the number of vertices then the algorithm will conclude that the graph is disconnected.
Cited by 0Open access
C. Patrascu, David-Traian Iancu · 2023
Evolutionary algorithms are a subfield of artificial intelligence, which, even if deep learning is the leading factor nowadays, gained more attention in recent years, due to the increase of processing power. One of the most popular topics in this field is Neuroevolution, since is combines the advantages of evolutionary algorithms with the advantages of neural and deep neural networks. In this paper, we demonstrate how can NeuroEvolution of Augmenting Topologies (NEAT) be used for game playing. The agent that we trained was able to rapidly learn how to play 2 very popular games, namely Dinosaur Game and Flappy Bird, without the need of any specialized computational device (no GPUs were needed). Multiple experiments were conducted, for different scenarios, showing how changing the game environment can change the learning performance of the algorithm.
Cited by 1