A bibliometric analysis of Genetic Algorithms throughout the history

Dao, Son Duy; Abhary, Kazem; Marian, Romeo

doi:10.1016/j.cie.2017.06.009

Cited by 63 publications

(27 citation statements)

References 15 publications

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“…Given a defined number of terms N T , Equation 2represents a generic regression model in which α i and β i are optimized using the proposed GA, while the empirical coefficients k i and the constant term k 0 are obtained with standard linear regression. Figure 5 reports the procedure of the proposed algorithm as it is typically implemented: it consists of four steps, i.e., initialization, selection, crossover and mutation [36]. In this case, the crossover and mutation operators are used in parallel, in order to emphasize the gains in performance that can be achieved from the concurrent application of operators with different and complementary roles [37].…”

Section: Computational Proceduresmentioning

confidence: 99%

“…the mutation operator is used to avoid local convergence of the genetic algorithm by introducing random variation in the genome of some individuals [32,39]. In fact, while increasing the number of generations, even if the crossover rate is high, chromosomes become more and more similar to each other, therefore blocking diversity and preventing the occurrence of more powerful generations [36]. In particular, the mutation operator only starts after some new generations with a fixed probability of occurrence.…”

Section: Computational Proceduresmentioning

confidence: 99%

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An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process

2020

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This study proposes a genetic algorithm-optimized model for the control of the fatigue life of AISI 1040 steel components after a high-power diode laser hardening process. First, the effect of the process parameters, i.e., laser power and scan speed, on the fatigue life of the components after the laser treatment was evaluated by using a rotating bending machine. Then, in light of the experimental findings, the optimization model was developed and tested in order to find the best regression model able to fit the experimental data in terms of the number of cycles until failure. The laser treatment was found to significantly increase the fatigue life of the irradiated samples, thus revealing its suitability for industrial applications. Finally, the application of the proposed genetic algorithm-based method led to the definition of an optimal regression model which was able to replicate the experimental trend very accurately, with a mean error of about 6%, which is comparable to the standard deviation associated with the process variability.

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Section: Computational Proceduresmentioning

confidence: 99%

Section: Computational Proceduresmentioning

confidence: 99%

An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process

2020

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“…The GA is a well‐known heuristic approach that finds near‐optimal solutions for NP‐hard problems in the large search spaces . Flexibility, the ability to work with continuous and discrete variables, handling large search space, providing multiple good solutions, and potential for applying parallel computing techniques are the advantages of this method compared to other meta‐heuristics to reduce the processing time . However, it simply gets stuck at a local optimum and often has slow convergent speed .…”

Section: Service Composition Strategiesmentioning

confidence: 99%

“…130 Flexibility, the ability to work with continuous and discrete variables, handling large search space, providing multiple good solutions, and potential for applying parallel computing techniques are the advantages of this method compared to other meta-heuristics to reduce the processing time. 131 However, it simply gets stuck at a local optimum and often has slow convergent speed. 132 Due to the advantages of GA, it has been broadly applied in the fields of computer science, social science, and engineering.…”

Section: Summary Of the Reviewed Bco-based Techniquesmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Int J Communication

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Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.

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“…The genetic algorithms are based on the mechanisms of natural selection and natural genetics [30]. The robustness of these algorithms on complex problems has led to an increasing number of applications in the field of artificial intelligence, numerical and combinatorial optimization, computer science, and engineering [31]. The genetic algorithms became popular through the work of John Holland and particularly his book Adaptation in Natural and Artificial Systems [32].…”

Section: Evolutionary Computation For Globalmentioning

confidence: 99%

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

Picos

Orozco-Rosas

Díaz-Ramírez

et al. 2018

Mathematical Problems in Engineering

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In this paper, we propose an evolutionary correlation filtering approach for solving pose estimation in noncontinuous video sequences. The proposed algorithm computes the linear correlation between the input scene containing a target in an unknown environment and a bank of matched filters constructed from multiple views of the target and estimates of statistical parameters of the scene. An evolutionary approach for finding the optimal filter that produces the highest matching score in the correlator is implemented. The parameters of the filter bank evolve through generations to refine the quality of pose estimation. The obtained results demonstrate the robustness of the proposed algorithm in challenging image conditions such as noise, cluttered background, abrupt pose changes, and motion blur. The performance of the proposed algorithm yields high accuracy in terms of objective metrics for pose estimation in noncontinuous video sequences.

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A bibliometric analysis of Genetic Algorithms throughout the history

Cited by 63 publications

References 15 publications

An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process

An Optimal Genetic Algorithm for Fatigue Life Control of Medium Carbon Steel in Laser Hardening Process

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Pose Estimation in Noncontinuous Video Sequences Using Evolutionary Correlation Filtering

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