Search Results - (( initial optimization based algorithm ) OR ( using classification based algorithm ))
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Improved cuckoo search based neural network learning algorithms for data classification
Published 2014“…Specifically, 6 benchmark classification datasets are used for training the hybrid Artificial Neural Network algorithms. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…The classification of estimation-based metaheuristic algorithms has been introduced for solving optimization problems. …”
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Finite impulse response optimizers for solving optimization problems
Published 2019“…The classification of estimation-based metaheuristic algorithms has been introduced for solving optimization problems. …”
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). …”
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…Also a new algorithm for finding the initial point is proposed. …”
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An ensemble data summarization approach based on feature transformation to learning relational data
Published 2015“…A ensemble clustering is designed, used and evaluated to generate the final classification framework that will take all input generated from the GA based clustering with Feature Selection and Feature Construction algorithms and perform the classification task for the relational datasets. …”
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An Improved Grey Wolf Optimization-based Learning of Artificial Neural Network for Medical Data Classification
Published 2021“…Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. …”
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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis
Published 2019“…To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. …”
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Automatic Segmentation and Classification of Skin Lesions in Dermoscopic Images
Published 2024“…For segmentation, the first proposed algorithm is based on the boundary condition model, which is tested over the ISIC dataset and achieved 96% of accuracy. …”
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RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Published 2024journal::journal article -
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Recent advances in meta-heuristic algorithms for training multilayer perceptron neural networks
Published 2025“…Key contributions include a comparative analysis of evolutionary, swarm intelligence, physics-based, human-inspired algorithms, and hybrid approaches benchmarked on classification datasets. …”
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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System
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Using genetic algorithms to optimise land use suitability
Published 2012“…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Based on these results, this paper aims to provide insights into the strengths and limitations of each optimizer, highlighting the importance of considering task-specific requirements when selecting an optimization algorithm for deep learning models.…”
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Nature-Inspired Drone Swarming for Wildfires Suppression Considering Distributed Fire Spots and Energy Consumption
Published 2024“…Our quantitative tests show that the improved model has the best coverage (95.3%, 84.3% and 65.8%, respectively) compared to two other methods Levy Flight (LF) algorithm and Particle Swarm Optimization (PSO), which use the same initial parameter values. …”
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