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A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising
Published 2015“…A modified crossover scheme called rand-length crossover was designed to fit the proposed variable-length DE, and the new DE algorithm is referred to as the random variable-length crossover differential evolution (rvlx-DE) algorithm. …”
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…A systematic method for selecting the ANN's input variables was developed using Matlab Programming language.…”
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Optimized differential evolution algorithm for linear frequency modulation radar signal denoising
Published 2013“…The main contention of this thesis is to investigate the development of new optimization technique based on Differential Evolution algorithm (DE), applied for radar signal denoising application. …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
Published 2018“…Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. …”
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A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. …”
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Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…The Seed Disperser Ant Algorithm (SDAA) is developed based on the evolution or expansion process of Seed Disperser Ant (Aphaenogaster senilis) colony. …”
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Crossover and mutation operators of genetic algorithms
Published 2017“…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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Crossover and mutation operators of genetic algorithms
Published 2017“…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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Direct Tuning Phase-Shift Angle In Dab Dc-Dc Converter Using Moth Flame Optimization (Mfo) Algorithm
Published 2022“…With aforementioned results, the MFO algorithm have well-functioning in DAB system.…”
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Shear strength test on unsaturated soil using natural microscopic surface tension force / Basharudin Abdul Hadi
Published 2017“…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network 29 30 base of the specimen until the flow ceased..…”
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Computational biology: where nature meets technology / Ts Faikah Awang @ Ismail and Ts Mohamad Shahrimi Hashim
Published 2023“…It all began in the early 1970s when basic artificial intelligence was developed to create the neural network model for the human brain to generate new algorithms. …”
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Optimization of emulsion polymerization of styrene and methyl methacrylate (MMA)
Published 2013“…Using gPROMS, the system analyzed the data, created models, developed algorithms, manipulated and plotted based on the functions and data. …”
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Modelling and simulation of drying process for ceramic membrane fabrication
Published 2015“…Due to the limitation in experimental or empirical ability to determine the changes of dynamic critical variables during drying, modelling and simulation seems to be the right option to determine and investigate these nonlinear and dynamic variables and will be the focus of this study. …”
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SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid
Published 2017“…There were two sets of algorithms in developing the UPLRB ANN model and every algorithm set consisted of model inputs data preparation, neural network script and neural network error checking measures. …”
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