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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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2
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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3
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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4
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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5
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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6
Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms
Published 2023“…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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7
Differential evolution for neural networks learning enhancement
Published 2008“…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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8
Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization
Published 2013“…These techniques have been used to optimize control points and weights in the description of spline functions used. …”
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9
Optimal location and size of distributed generation to reduce power losses and improve voltage profiles using differential evolution optimization method
Published 2016“…The results obtained by using the DE method were compared with those obtained by genetic algorithm (GA) method. …”
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10
Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies
Published 2011“…Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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12
Optimization Of Fractional-Slot Permanent Magnet Synchronous Machine Using Analytical Sub-Domain Model And Differential Evolution
Published 2019“…From the results obtained, the Analytical Sub-Domain Differential Evolution Algorithm (ASDEA) has better optimization technique capability compared with Analytical Sub-Domain Particle Swarm Optimization (ASPSO). …”
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13
Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The unified RBF model is then used for autotuning the PID controller using the DE algorithm. …”
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Optimized PID controller of DC-DC buck converter based on archimedes optimization algorithm
Published 2023“…The proposed PID controller, optimized using AOA, is contrasted with PID controllers tuned via alternative algorithms including the hybrid Nelder-Mead method (AEONM), artificial ecosystem-based optimization (AEO), differential evolution (DE), and particle swarm optimizer (PSO). …”
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15
OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM
Published 2011“…Many tuning methods for PID controller have been developed, and one of them is based on natural evolution, the genetic algorithm (GA). …”
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16
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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17
Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The unified RBF model is then used for autotuning the PID controller using the DE algorithm. …”
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Differential evolution based special protection and control scheme for contingency monitoring of transmission line overloading
Published 2017“…In this paper, a Special Protection and Control Scheme (SPCS) based on Differential Evolution (DE) algorithm for optimal generation rescheduling to mitigate line overloads in contingency conditions has been presented. …”
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Dynamic smart grid communication parameters based cognitive radio network
Published 2023“…A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. …”
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Crossover and mutation operators of real coded genetic algorithms for global optimization problems
Published 2016“…The rationale behind developing algorithms using real encoding of chromosome representations is the limitations of binary encoding. …”
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