A review of Genetic Algorithms and Parallel Genetic Algorithms on Graphics Processing Unit (GPU)

Johar, Fauzi Mohd; Azmin, Farah Ayuni; Suaidi, M. K.; Shibghatullah, Abdul Samad; Ahmad, B. H.; Salleh, S. N.; Aziz, Mohamad Zoinol Abidin Abd; Shukor, M. Md

doi:10.1109/iccsce.2013.6719971

Cited by 12 publications

(5 citation statements)

References 17 publications

(15 reference statements)

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“…From the previous paper work [4,5], it is observed that Parallel Genetic Algorithm can be easily map on GPU than other prediction algorithms. Time required to find fitness function is very less than to find mapping function in SVM.…”

Section: Observationsmentioning

confidence: 99%

“…[3] Some common tools for prediction include: neural networks, regression, Support Vector Machine (SVM), and discriminant analysis. Recently, data mining techniques such as neural networks, fuzzy logic systems, genetic algorithms and rough set theory are used to predict control and failure detection tasks [4]. In this paper, the algorithms will forecast a probability for the given data situation.…”

Section: Prediction Algorithmmentioning

confidence: 99%

“…The fitness function is defined such that it takes an individual as its input and gives its fitness value. It is used to find the optimal combination of considered parameters so that prediction of future things will be accurate [4]. To increase the performance of GA, crossover and mutation steps are applied in parallel.…”

Section: Parallel Genetic Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

Kulkarni¹

2016

IJECS

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Nowadays, the persuit of prediction of chemical bond formation among researchers is evident to discover new drugs or for new discovery of chemicals. Predictive modeling gives statistics to predict outcomes. Most of time the event one wants to predict is in the future, but predictive modeling applicable to any type of event, regardless of when it occurred. In this paper, we focused on comparative analysis of various Prediction algorithms to estimate the best algorithm for the prediction of chemical bond formation with observations. The performances of these techniques are compared and it is observed that parallel Genetic Algorithm provides better performance results in accuracy and speedup as compared to other prediction techniques on GPU.

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Section: Observationsmentioning

confidence: 99%

Section: Prediction Algorithmmentioning

confidence: 99%

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Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

Kulkarni¹

2016

IJECS

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“…Hybrid a search method of genetic algorithm can improve the search performance. Fauzi Mohd Johar, Farah Ayuni Azminand et al [9] studies GA and parallel GA and analyses its usage in central Processing Unit (CPU) and Graphics Processing Unit (GPU).…”

Section: Performance Of Gamentioning

confidence: 99%

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

Rani¹,

Kumar²

2017

International Journal of Advanced Research in Computer and Comm

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Classification is one of the important areas of research in the field of data mining and machine learning. This paper discusses about GA, ANN and SVM algorithm and their use in classification. The artificial neural network is the widely used technique for classification and prediction. ANN has some disadvantage such long learning rate, high computational cost, convergence at local optima and adjustment of weight. Optimization techniques and hybridization improve ANN performance. GA is an optimization technique that produces optimization of the problem by using natural evolution. SVM use the nonlinear kernel functions that implicitly map input data into high-dimensional feature spaces. Hybridization is a technique which combines the two or more classifier to improve the performance of the classifier.

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“…They discussed the features of the GPU and the relevant issues when implementing parallel genetic algorithms. Johar et al [13] conducted an analysis of genetic algorithms implemented in parallel both CPU and GPU using CUDA [14] architecture. The analysis was performed by comparing the operations performed in both implementations.…”

Section: Introductionmentioning

confidence: 99%

A coarse-grained parallelization of genetic algorithms

Rathomi

Pulungan

2018

Int. J. Adv. Intell. Informatics

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Genetic algorithms are frequently used to solve optimization problems. However, the problems become increasingly complex and time consuming. One solution to speed up the genetic algorithm processing is to use parallelization. The proposed parallelization method is coarse-grained and employs two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed approach is similar to the sequential genetic algorithm. Parallelization with coarse-grained method, however, can improve the processing and convergence speed of genetic algorithms.

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A review of Genetic Algorithms and Parallel Genetic Algorithms on Graphics Processing Unit (GPU)

Cited by 12 publications

References 17 publications

Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

Survey :Various Prediction Algorithms for Chemical Bond Formation on GPU

A Survey: Hybrid Intelligent Modeling Technique for Data Classification

A coarse-grained parallelization of genetic algorithms

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