Results 161 to 170 of about 237,312 (198)
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Genetic Algorithm (GA) Methodology and Its Internal Working
2021Many practitioners are shy with implementing GAs. Due to this, a lot of researchers avoid using GAs as problem-solving techniques. It is desirable that an implementer of GA must be familiar in working with high-level computer languages. Implementation of GA involves complex coding and intricate computations which are of a repetitive nature.
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A genetic algorithm for the 1D electron gas
Physica A: Statistical Mechanics and its Applications, 2008Abstract We show how to apply a genetic algorithm to describe the homogeneous electron gas. For simplicity we consider just the 1D case. The pair correlation function so obtained is compared with those found by using variational Monte Carlo and quantum hypernetted chain calculations and reported for the first time in this paper.
César O. Stoico +2 more
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Improving Primary School Timetabling Using Genetic Algorithm GA
SSRN Electronic Journal, 2011Genetic Algorithm (GA) is one of the most popular optimization solutions. It has been implemented in various applications such as scheduling. Badly designed timetables are not just inconvenient but proved expensive in terms of wasting time and money.
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VNR-GA: Elastic virtual network reconfiguration algorithm based on Genetic metaheuristic
2013 IEEE Global Communications Conference (GLOBECOM), 2013Cloud Computing offers elasticity and enhances resource utilisation. This is why its success strongly depends on the efficiency of the physical resource management. This paper deals with dynamic resource reconfiguration to achieve high resource utilisation and to increase Cloud providers income.
Dab, Boutheina +3 more
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Fractal coding in genetic algorithm (GA) antenna optimization
IEEE Antennas and Propagation Society International Symposium 1997. Digest, 2002Genetic algorithms are an excellent approach to optimization problems in electromagnetics. Their chief benefit is the simplicity of coding and operation of the engine, which emulates aspects of Darwinian evolution to decide on the suitability of a set of antenna model parameters.
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Understanding Genetic Algorithm (GA) Operators Step by Step
2021In today's world of soft computing, GAs are a hot topic. Researchers developed this fascinating application to face or to counter many difficult problems which cannot be solved through traditional approaches. We have seen that in the published work of GAs, an author does not reveal the working of the GA as a whole. In this chapter, the authors tried to
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SAREC-GA: A Security-Aware Real-Time Scheduling Algorithm with Genetic Algorithm
2007 International Conference on Machine Learning and Cybernetics, 2007Security requirements of security-critical real-time applications must be met in addition to satisfying timing constraints. However, conventional real-time scheduling algorithms ignore the applications' security requirements. Tao Xie, Xiao Qin et al. have proposed a security-aware scheduling strategy, or SAREC, and scheduling algorithm SAREC-EDF, which
Sheng-Wu Xiong, Yong-Xiang Zhao, Ning Xu
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QCs scheduling scheme of genetic algorithm (GA) and improved firefly algorithm (FA)
Cluster Computing, 2018As an important part of container logistics, quay cranes (QCs) are crucial equipment in multimodal container transportation. The scheduling and allocation of QCs determine the operational efficiency of container terminals. By analyzing the way quay cranes are operated, this paper establishes a mixed-integer dynamic rolling-horizon programming model for
Liangcai Dong, Yang Yang, Siyun Sun
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Music Composition Using Genetic Algorithms (GA) and Multilayer Perceptrons (MLP)
2005In this work, authors have developed a system which is capable of composing songs using Genetic Algorithms (GA) to evolve melody and rhythm. Each GA uses two Multilayer Perceptron (MLP) type artificial neural networks (ANN) to judge for the fitness of individuals in the population.
Pigg, P, Dixit, V, Goksu, H
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