Search Results - (( developing feature evolution algorithm ) OR ( java implementation bees algorithm ))
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Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity
Published 2018“…Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
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A New Quadratic Binary Harris Hawk Optimization For Feature Selection
Published 2019“…In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). …”
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Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
Published 2017“…The experimental results reveal that the developed algorithm is able to achieve an accuracy of 87.82% F1-measure based on images from the ECU database. …”
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Intersection Features For Android Botnet Classification
Published 2019“…This paper proposed an enhancement approach for Android botnet classification based on features selection and classification algorithms. The proposed approach used requested permissions in the Android app and API function as features to differentiate between the Android botnet apps and benign apps. …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio...
Published 2023“…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution
Published 2010“…For this research, three Evolutionary Algorithm (EA) (Genetic Algorithm (GA), Differential Evolution (DE), and POE) is use to evolve a Feed-Forward Artifitial Neural Networks (FFANN) to playa custom made map in Warcraft III and the outcome is compared amng them. …”
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Crossover and mutation operators of genetic algorithms
Published 2017“…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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Crossover and mutation operators of genetic algorithms
Published 2017“…Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. …”
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Software testing optimization for large systems using agent-based and NSGA-II algorithms
Published 2023“…The performance of a multi-objective Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and evolutionary multi-agent system (EMAS) on Feature Models (FMs) to enhance large System testing is reported in this study.…”
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A guided hybrid k-means and genetic algorithm models for children handwriting legibility performance assessment / Norzehan Sakamat
Published 2021“…Euclidean Distance, Pearson Correlation and Matching Matrix were used to measure the performance of the feature extraction and clustering methods. Recognition software achieved 87.14%, EPD algorithm achieved 73.57% and HMT algorithm achieved 74.30%) prediction accuracy with OTs. …”
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Chaotic map initializations with Tiki-Taka algorithm for software remodularization problem
Published 2021“…During its lifecycle, software system often evolves to reflect the bug fixes and upgrades as well as the addition/removal of features from its users. As the evolution process takes place, the modularization becomes complex and gradually loose its quality. …”
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Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game
Published 2015“…The proposed EC methods are Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), and Pareto-based Differential Evolution (PDE). …”
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iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
Published 2024“…In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. …”
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Diagnosis of voice disorders using band energy spectrum in wavelet domain
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Quality of service and scheduling performance optimization in LTE networks / Abubakar Auwal Idris
Published 2017“…The developed algorithm is simulated in LTE-Sim simulator released 5 in terms of three QoS metrics (1) Packet Loss Ratio (2) Delay (3) Throughput. …”
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Time series forecasting of energy commodity using grey wolf optimizer
Published 2015“…The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices.In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting.The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price.Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE).Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. …”
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Handbook of research on smart computing for renewable energy and agro-engineering
Published 2019“…The rise in population and the concurrently growing consumption rate necessitates the evolution of agriculture to adopt current computational technologies to increase production at a faster and smoother scale. …”
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