Search Results - (( developing models genetic algorithm ) OR ( java code classification algorithm ))

Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. …”
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Model structure selection for a discrete-time non-linear system using genetic algorithm by Ahmad, Robiah, Jamaluddin , Hishamuddin, Hussain, Mohd. Azlan

“…The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

“…The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

“…The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. …”
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Model structure selection for a discrete-time non-linear system using a genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, Mohd Azlan

“…The performance of the proposed algorithm is also compared to the model developed using the orthogonal least squares (OLS) algorithm. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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The compact genetic algorithm for likelihood estimator of first order moving average model by Al-Dabbagh, R.D., Baba, M.S., Mekhilef, Saad, Kinsheel, A.

“…The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. …”
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition by Yahya, Anwar Ali, Mahmod, Ramlan, Ramli, Abd Rahman

“…In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks' random variables. …”
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Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm by Nukman, Y., Hassan, M.A., Harizam, M.Z.

“…To overcome such problem, a hybrid genetic algorithm-based Taguchi ANN (GA-Taguchi ANN) has been developed. …”
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Multivariable system identification for dynamic discrete-time nonlinear system using genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, M. A.

“…The development of a multivariable system identification model for dynamic discrete-time nonlinear system using genetic algorithm was discussed and analysed. …”
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Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm by Al Hazza, Muataz, Adesta, Erry Yulian Triblas, Superianto, M. Y., Riza, Muhammad

“…Machining of hard materials at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality. Thus, developing a model for estimating the cutting parameters and optimizing this model to minimize the cutting temperatures and surface roughness becomes utmost important to avoid any damage to the quality surface.This paper presents the development of new models and optimizing these models of machining parameters to minimize the cutting temperature in end milling process by integrating the genetic algorithm (GA) with the statistical approach. …”
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A study on regional GDP forecasting analysis based on radial basis function neural network with genetic algorithm (RBFNN-GA) for Shandong economy by Qing, Zhang, Abdullah, Abdul Rashid, Choo, Wei Chong, Ali, Mass Hareeza

“…This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.…”
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Multivariable system identification for dynamic discrete-time nonlinear system using genetic algorithm by Ahmad, R., Jamaluddin, H., Hussain, Mohd Azlan

“…The development of a multivariable system identification model for dynamic discrete-time nonlinear system using genetic algorithm was discussed and analysed. …”
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Development of genetic algorithm-based fuzzy rules design for metal cutting data selection by Wong, S.V, Hamouda, A.M.S

“…The development of a Fuzzy Genetic Optimization algorithm is presented and discussed. …”
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Comparison between multi-objective and single-objective optimization for the modeling of dynamic systems by Mohd Zakimi, Zakaria, Hishamuddin, Jamaluddin, Robiah, Ahmad, Sayed Mohammad Reza, Loghmanian

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Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

“…On the other hand, the superior performance of the genetic algorithm that implements an extended diversity control mechanism demonstrates that more competent genetic algorithms can be designed through customized operators. …”
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Job shop rescheduling using a hybrid artificial immune system and genetic algorithm model by Mohamed Din, Aniza, Ku-Mahamud, Ku Ruhana, Yusof, Yuhanis, Mahmuddin, Massudi

“…This paper discusses on developing a hybrid model to tackle the problem of changing environment in the job shop scheduling problem.The main idea is to develop building blocks of partial schedules using the model developed that can be used to provide backup solutions when disturbances occur during production.This model hybridizes genetic algorithm (GA) with artificial immune systems (AIS) techniques to generate these partial schedules.Each partial schedule, also known as antibody, is assigned a fitness value for the selection of final population of best partial schedules. …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…Build up a user interface by using Visual Studio Code (VSC) to run the coding of Cascading Style Sheet (CSS), Hyper Text Markup Language (HTML) and JavaScript (JS) as a webpage and connect to Azure Machine Learning Model and this will allow the user from using the model from a webpage when they have active internet with any devices.…”
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Assignation of PSM evaluator using genetic algorithm by Yap, Suet Lee

“…The purpose of this paper is to present a design of development for Assignation of PSM Evaluator using Genetic Algorithm(APEGA)system.This is an application system that is used to assist the Faculty of Computer System and Software Engineering(FSKKP)of University Malaysia Pahang(UMP)in matching the optimum evaluators for the students in PSM presentation carnival.In the methodology part,a development model which involves with client participation is designed in order to use in the development of this project.The target user of the system is PSM coordinator who is responsible in assigning the PSM evaluator.Assignation of PSM Evaluator using Genetic Algorithm APEGA)is expected to be able in developing a well-distributed matching and overcoming the relevant constraints in an intelligent way. …”
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