Search Results - (( based optimization method algorithm ) OR ( data distribution selection algorithm ))
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Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…The proposed solution to this problem is based on a general combinatorial optimization algorithm known as Genetic Algorithm, and the load flow equations in distribution network. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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Thesis -
3
Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…The proposed framework and models that are are considered to be the specific research contributions of this thesis are: 1) a comparison framework of classification models for software defect prediction known as CF-SDP, 2) a hybrid genetic algorithm based feature selection and bagging technique for software defect prediction known as GAFS+B, 3) a hybrid particle swarm optimization based feature selection and bagging technique for software defect prediction known as PSOFS+B, and 4) a hybrid genetic algorithm based neural network parameter optimization and bagging technique for software defect prediction, known as NN-GAPO+B. …”
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4
Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…This study investigates two different issues of performance measure in data classification problem. First, this study examines the use of accuracy measure as a discriminator for building an optimized Prototype Selection (PS) algorithm. …”
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Traditional marble game using ant colony optimization / Muhammad Izzat Imran Che Isa
Published 2017“…In future, traditional marble game can be applied with other search algorithm to optimize the solution…”
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7
Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman
Published 2025“…This study applies Genetic Algorithms (GA) to optimize space distribution for test scheduling, addressing the challenge of managing multiple test sessions across distinct locations. …”
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A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…Algorithmic level based methods however are based on introducing new optimization task to improve the minority class classification rate, without changing the data characteristics. …”
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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
Published 2023Conference Paper -
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Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty
Published 2024“…Further more, an improved non-dominated sorting genetic algorithm with an elite strategy II (IMNSGA-II) has been developed to solve the two bi-objective models, surpassing existing literature’s algorithms such as Pareto Envelope-based Selection Algorithm II (PESA-II) and NSGA-II. …”
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11
Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction
Published 2024“…To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). …”
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Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation
Published 2024“…Unlike benchmark algorithms that rely on static VM selection or post-hoc relocation of cloudlets, the EDLB algorithm dynamically identifies optimal cloudlet placement in real-time. …”
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Controller placement problem in the optimization of 5G based SDN and NFV architecture
Published 2021“…A heuristic called dynamic mapping and multi-stage CPP algorithm (DMMCPP) was developed to solve CPP as resource allocation in a distributed 5G-SDN-NFV-based network. …”
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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“…In order to train Ridgelet probabilistic neural network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is introduced. …”
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Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
Published 2018“…In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.…”
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16
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Sensitivity-based fuzzy multi-objective portfolio model with Value-at-Risk
Published 2019“…In addition, compared with the VaR-FMOPSM model, our sensitivity-based improved model with the IPSO algorithm also performs better than Genetic Algorithm and Simulate Anneal Algorithm (SA), it provides the same performance on this point. …”
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Technical report: genetic algorithm for solving capacitated vehicle routing problem / Mohd Faris Mohd Zaki and Muhammad Ammar Zulqornain Abdul Rashid
Published 2017“…From the data, we conduct iteration process which consist of crossover, selection and mutation process. …”
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Student Project -
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OFF-GRID SOLAR PHOTOVOLTAIC (PV) DESIGN BASED ON FUZZY TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) APPROACH
Published 2022“…The aforementioned technique is the MCDM method, particularly the fuzzy TOPSIS algorithm. This algorithm is embedded in the PV system to select the best PV components’ configuration based on multiple criteria. …”
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Final Year Project Report / IMRAD -
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Published 2024“…Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. …”
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