Genetic Algorithms

Yang, Xin‐She

doi:10.1016/b978-0-12-416743-8.00005-1

Cited by 193 publications

(137 citation statements)

References 7 publications

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“…Genetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications (Yang, 2014). Peng and Song, 2010) have been solved using genetic algorithms.…”

Section: Ga-based Job Schedulermentioning

confidence: 99%

Job scheduling in the Expert Cloud based on genetic algorithms

et al. 2014

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Purpose -Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people by employing internet infrastructures and Cloud computing concepts without any information of their location. Job scheduling is one of the most important issue in Expert Cloud and impacts on its efficiency and customer satisfaction. The purpose of this paper is to propose an applicable method based on genetic algorithm for job scheduling in Expert Cloud. Design/methodology/approach -Because of the nature of the scheduling issue as a NP-Hard problem and the success of genetic algorithm in optimization and NP-Hard problems, the authors used a genetic algorithm to schedule the jobs on human resources in Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on response time; one point crossover and swap mutation are also used. Findings -The results indicate that the proposed method can schedule the received jobs in appropriate time with high accuracy in comparison to common methods (First Come First Served, Shortest Process Next and Highest Response Ratio Next). Also the proposed method has better performance in term of total execution time, service þ wait time, failure rate and Human Resource utilization rate in comparison to common methods. Originality/value -In this paper the job scheduling issue in Expert Cloud is pointed out and the approach to resolve the problem is applied into a practical example.

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Section: Ga-based Job Schedulermentioning

confidence: 99%

Job scheduling in the Expert Cloud based on genetic algorithms

et al. 2014

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“…1, while its brief description is as it follows. The automatic configuration of various ANN parameters was performed by using Genetic Algorithm (GA) [46], following Algorithm 1 (Fig. 1a) [35].…”

Section: Automated Development Of Annsmentioning

confidence: 99%

Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery

Vukićević

Stojadinović

Radović

et al. 2016

Computers in Biology and Medicine

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“…This method provides a common framework for solving optimization problems of complex systems, thus it has a wide range of applications in industrial control, data mining, economic management, robotics, image processing [13][14].…”

Section: Identification Of Oils Based On Svm and Gamentioning

confidence: 99%

NIR Spectroscopy and NMF Algorithm for Identification of Oil Pollutants in Water

Tan¹,

Zhao²,

Guo³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-Oil pollutants is one of the major pollution sources in water. Accurate, rapid, and convenient detection method of oil pollutants in water has very important theoretical value and practical significance. The combination of near-infrared spectroscopy (NIR) and chemometrics is ideal for such a situation. NIR spectroscopy is a powerful and effective technique. traditional NIR methods do not take full account of the absorbance data non-negative characteristics, resulting in the analysis lack of reasonable explanation. In this paper, the qualitative discriminate method of single species oil contaminants based on nonnegative matrix factorization feature extraction combined with support vector machine classification algorithm is studied. Non-negative matrix factorization algorithm and support vector machine classifier parameters on classification accuracy are discussed in depth to optimize NIR qualitative classification model. The present method has a good identification effect and strong generalization ability ， and can work as a new method for rapid identification of oil pollutants in water．

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Genetic Algorithms

Cited by 193 publications

References 7 publications

Job scheduling in the Expert Cloud based on genetic algorithms

Job scheduling in the Expert Cloud based on genetic algorithms

Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery

NIR Spectroscopy and NMF Algorithm for Identification of Oil Pollutants in Water

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