Results 181 to 190 of about 12,515 (232)

Biased Random-Key Genetic Algorithm for Structure Learning

2021
The structure learning of Bayesian networks is a NP-hard problem, which cannot be easily solved since it is usually a complex combination optimization problem. Thus, many structure learning algorithms using evolutionary techniques are investigated recently to obtain a reasonable result.
Baodan Sun, Yun Zhou
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A Dynamic Programming Offloading Algorithm Using Biased Randomization

2016 IEEE 9th International Conference on Cloud Computing (CLOUD), 2016
Computational offloading can improve the energy efficiency of mobile devices, by executing some tasks of a mobile application in the cloud. In this paper, a new algorithm called 'Dynamic Programming with Randomization' (DPR) is presented. The DPR algorithm iteratively improves an offloading decision vector, by generating random bit strings with a ...
Haleh Shahzad, Ted H. Szymanski
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A personalized recommendation algorithm via biased random walk

2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2014
With the rapid development of Internet, Recommender Systems can help us efficiently find the useful objects in the information era. Generally, the traditional random walk algorithm has high accuracy but low personality and diversity. In this paper, we propose an improved random walk algorithm by depressing the influence of large-degree objects ...
Da-Cheng Nie, Yan Fu, Jun-Lin Zhou, Zhen Liu, Zi-Ke Zhang, Chuang Liu   +5 more
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Biased random-key genetic algorithms for combinatorial optimization

Journal of Heuristics, 2010
Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154---160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms ...
José Fernando Gonçalves, Mauricio G. C. Resende   +1 more
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Biased random-key genetic algorithm for linearly-constrained global optimization

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
Global optimization seeks a minimum or maximum of a multimodal function over a discrete or continuous domain. In this paper, we propose a biased random key genetic algorithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to nonlinear constraints.
Ricardo M. A. Silva, Mauricio G. C. Resende, Panos M. Pardalos, Joao L. Faco   +3 more
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D-BRKGA: A Distributed Biased Random-Key Genetic Algorithm

2017 IEEE Congress on Evolutionary Computation (CEC), 2017
Despite the use of genetic algorithms in many optimization problems, many new versions were proposed since them, from distributed versions of the canonical genetic algorithm (GA) to more restructured evolutions like the Biased Random-Key Genetic Algorithm (BRKGA).
Bruno Ferreira De Faria Alixandre, Marcio Dorn   +1 more
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A Biased Random-Key Genetic Algorithm for Bandwidth Reduction

2020
The bandwidth minimization problem is a well-known \(\mathcal {NP}\)-hard problem. This paper describes our experience in implementing a biased random-key genetic algorithm for the bandwidth reduction problem. Specifically, this paper compares the results of the new algorithm with the results yielded by four approaches. The results obtained on a set of
P. H. G. Silva, D. N. Brandão, I. S. Morais, S. L. Gonzaga de Oliveira   +3 more
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A biased random-key genetic algorithm for data clustering

Mathematical Biosciences, 2013
Cluster analysis aims at finding subsets (clusters) of a given set of entities, which are homogeneous and/or well separated. Starting from the 1990s, cluster analysis has been applied to several domains with numerous applications. It has emerged as one of the most exciting interdisciplinary fields, having benefited from concepts and theoretical results
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A biased random-key genetic algorithm for road congestion minimization

Optimization Letters, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Buriol, Luciana S., Hirsch, Michael J., Pardalos, Panos M., Querido, Tania, Resende, Mauricio G. C., Ritt, Marcus   +5 more
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