Search Results - (evolutionary OR evolution) ((learning techniques) OR (selection techniques))
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Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository.…”
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Broadening selection competitive constraint handling algorithm for faster convergence
Published 2020“…The median convergence traces have been compared with two different algorithms based on differential evolution, i:e: Ensemble of Constraint Handling Techniques (ECHT) and Stochastic Ranking Differential Evolution (SRDE). …”
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Hybrid evolutionary programming-artificial neural network-based lightning prediction system / Dalina Johari
Published 2009“…It involved the development of ANN design and embedding EP optimization technique for optimizing selected ANN parameters in order to improve the system's generalization capability.ANN, an intelligent machine learning technique, is inspired by the way our biological nervous systems process information. …”
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A study on biomolecular sequence alignment using machine learning techniques
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. …”
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An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…In recent years, detection methods based on machine learning techniques are widely deployed in order to improve the efficiency of anomaly-based detection. …”
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Minimization of power loss by evolutionary programming using Thyristor Controlled Series Compensators (TCSC) / Fazleza Abdul Latiff
Published 2007“…The evolution of solution is carried out through mutation and competitive selection. …”
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PMT: opposition-based learning technique for enhancing meta-heuristic performance
Published 2019“…Addressing these issues, this research proposes a new general opposition-based learning (OBL) technique inspired by a natural phenomenon of parallel mirrors systems called the parallel mirrors technique (PMT). …”
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Game-theoretic differential evolution for multiobjective optimization of green sand mould system
Published 2016“…In addition the standard metaheuristic, differential evolution is improved using concepts from evolutionary game theory. …”
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Artificial intelligence based technique for classification of incipient faults in power transformer based on dissolved gas analysis (DGA) method / Fathiah Zakaria
Published 2014“…This thesis presents the development of an Evolutionary Programming (EP) - Taguchi Method (TM) - Artificial Neural Network (ANN) based technique for the (DGA) method based on historical industrial data. …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. Unlike existing OBL-based techniques, the PMT generates more than one candidate in multiple solution-space directions. …”
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Harmonic Elimination Pulse Width Modulation Using Differential Evolution Technique For Three Phase Voltage Source Inverter
Published 2018“…Differential Evolution (DE) has been gaining popularity among researchers as an effective yet simple evolutionary algorithm to solve the optimization problems. …”
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New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab. Aziz
Published 2014“…Secondly, a new voltage stability prediction technique utilising state of the art machine learning, Support Vector Machine (SVM) was developed. …”
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A novel hybrid artificial intelligence technique for colpitts oscillator design
Published 2014“…Various traditional approaches such as intuitive reasoning, mathematical calculation, and simulation-based techniques have been proposed in the literature for this purpose. …”
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation
Published 2019“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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