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Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Published 2024“…The proposed model was developed and implemented using MATLAB software. …”
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Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach
Published 2025“…This paper proposes a new approach using convolutional neural networks with genetic algorithms for estimating air quality directly from images. …”
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Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant
Published 2019“…Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. …”
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A New Parallel Genetic Algorithm Model
Published 2020“…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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7
Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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A genetic algorithm for testable data path synthesis
Published 2017“…The approach is formulated as an allocation problem and solved using an efficient genetic algorithm that generates cost-effective testable designs. …”
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A new genetic algorithm approach for unit commitment
Published 1997“…This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. …”
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A neural networks algorithm for data path synthesis
Published 2003“…The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. …”
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An incremental approach for test scheduling and synthesis using genetic algorithms
Published 2017“…The method is based on a genetic algorithm that efficiently explores the testable design space. …”
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12
Concurrent BIST Synthesis and Test Scheduling Using Genetic Algorithms
Published 2007“…The method is based on a genetic algorithm that efficiently explores the testable design space and finds a sub-optimal test registers assignment for each k-test session. …”
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A method for the minimum coloring problem using genetic algorithms
Published 2006“…The algorithm was implemented and tested on various set of instances including large DIMACS challenge benchmark graphs, all yielding favorable results.…”
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A genetic-based algorithm for fuzzy unit commitment model
Published 2000“…The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …”
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A Parallel Neural Networks Algorithm for the Clique Partitioning Problem
Published 2002“…In this paper we present a parallel algorithm to solve the above problem for arbitrary graphs using a Hopfield Neural Network model of computation. …”
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Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem
Published 1999“…A new implementation of the genetic algorithm is introduced. …”
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Nonlinear model predictive control of Hammerstein and Wiener modelsusing genetic algorithms
Published 2001“…In this paper a novel approach for the implementation of nonlinear MPC is proposed using genetic algorithms (GAs). …”
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A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†
Published 2010“…The proposed algorithm has a time complexity of O(1) for a neural network with n vertices and k colours. …”
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Estimation of the methanol loss in the gas hydrate prevention unit using the artificial neural networks: Investigating the effect of training algorithm on the model accuracy
Published 2023“…Adjusting the weight and bias of the ANN model using an optimization algorithm is known as the training process. …”
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Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification
Published 2025“…The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. …”