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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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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
Logic Programming In Radial Basis Function Neural Networks
Published 2013“…In this thesis, I established a new method for determining the best number of the hidden neurons in radial basis function neural networks. …”
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8
Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja
Published 2004“…The steps to calculate a shortest path using A • algorithm is shown by using appropriate examples and related figures. …”
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9
The solution of 2048 puzzle game using A* algorithm / Zafhira Ismail
Published 2017“…The output of this project is the best solution to achieve the goal using A* algorithm. …”
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10
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…Two methods of load flow solution with different accuracy are employed, i.e., simplified method, which uses the approximation of P and Q at the start of the implemented program, and fast-decoupled method, which gives an exact solution. …”
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11
An alternative method to solve combined economic emission dispatch problems using flower pollination algorithm
Published 2016“…To simplify the programming, objective of economic dispatch (ED) and emission dispatch (EmD) are combined into a single function by price penalty factor and analysed using weighted sum method to choose the best compromising result. …”
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12
Designing and Developing an Intelligent Congkak
Published 2011“…and “Can Min-Max algorithm (MM) be speeded up by using NN as a forward-pruning method?”. …”
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13
Enhancement of Ant System Algorithm for Course Timetabling Problem
Published 2009“…In order to develop a new algorithm for the course scheduling problem, this research follows the experimental research methodology that consist of problem analysis, designing algorithm, implementing algorithm as a computer program in order to examine the results, analyzing the results, and if necessary improving the algorithm by doing all those activities over and over again. …”
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14
Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model topology is designed using selection from the best training algorithm, transfer function, number of training runs (1000-5000), number of hidden layers (1-3) and nodes (5-15). …”
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15
Using genetic algorithms to optimise land use suitability
Published 2012“…The first task was studying the dominant of crops and economic suitability evaluation of land with the land evaluation framework developed by FAO, (1976-2007) using GIS. Second task is to determine the fitness function for the genetic algorithms. …”
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16
Small signal oscillatory instability control of power system using posicast controller and evolutionary programming
Published 2014“…The gain of this compensator is very important because, by optimizing it the performance of the posicast controller is improved. Two methods of optimization which are Evolutionary Programming (EP) and Genetic Algorithm (GA) were used to find the best value of the K and their comparative study were analyzed. …”
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Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris
Published 2019“…The proposed co-simulation process is developed by coupling building energy simulation (BES) software, Energy Plus with multi-objective evolutionary programming (MOEP) algorithm which is implemented in Matlab using coupling software, BCVTB. …”
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18
A new domain specific scripting language for automated machine learning pipeline
Published 2019“…This paper concerns on two difficulties faced by non-expertsâ�� users in the utilization of machine learning; design of the model and the programming task for implementation. From the varieties of the machine learning algorithms, selecting the best model with the best configurations is a critical and complex design issue. …”
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Implementation Of Hardware Software Partitioning In Embedded System
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