Search Results - optimal ((((((self algorithm) OR (svm algorithm))) OR (search algorithm))) OR (new algorithm))

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  1. 1

    LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar

    Published 2015
    “…In this study, a new metaheuristic algorithm, viz., Grey Wolf Optimizer (GWO), is employed to optimize the parameters of interest. …”
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    Conference or Workshop Item
  2. 2

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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    Thesis
  3. 3

    Malaysian menu planning model using Self-adaptive Hybrid Genetic Algorithm (SHGA) by Mohd Razali, Siti Noor Asyikin, Engku Abu Bakar, Engku Muhammad Nazri, Ku-Mahamud, Ku Ruhana, Arbin, Norazman, Rusiman, Mohd Saifullah

    Published 2018
    “…Hybridization technique of two local search methods and self-adaptive strategy has successfully improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme.…”
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    Article
  4. 4

    Malaysian menu planning model using Self-Adaptive Hybrid Genetic Algorithm (SHGA) by Mohd Razali, Siti Noor Asyikin, Engku Abu Bakar, Engku Muhammad Nazri, Ku Mahamud, Ku Ruhana, Arbin, Norazman, Rusiman, Mohd Saifullah

    Published 2018
    “…Hybridization technique of two local search methods and self-adaptive strategy has successfully improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme…”
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    Article
  5. 5

    Improvement on rooftop classification of worldview-3 imagery using object-based image analysis by Norman, Masayu

    Published 2019
    “…The result presents that the Ant-Search algorithm via LibSVM was determined as the best combination with 100% accuracy. …”
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    Thesis
  6. 6

    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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    Thesis
  7. 7

    Menu Planning Model for Malaysian Boarding School Using Self-Adaptive Hybrid Genetic Algorithms by Siti Noor Asyikin, Mohd Razali

    Published 2011
    “…Hybridization technique of local search method and self-adaptive strategy have improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme. …”
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    Thesis
  8. 8

    Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking by Nurul Izzatie Husna, Muhamad Fauzi

    Published 2023
    “…In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. …”
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    Thesis
  9. 9

    Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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    Article
  10. 10

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. …”
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    Thesis
  11. 11

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The adaptive repository mechanism is a general scheme and can be embedded with high flexibility into any population-based evolutionary algorithm. Moreover, this work is extended to integrate the SPX crossover operator with the adaptive ARDE algorithm in a new way of implementation in order to make the adaptive ARDE algorithm satisfy both the global and local search requirements. …”
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    Thesis
  12. 12

    Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices by Chiroma, Haruna, Ya’u Gital, Abdulsalam, Abubakar, Adamu, Usman, Mohammed Joda, Waziri, Usman

    Published 2014
    “…The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. …”
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    Proceeding Paper
  13. 13

    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

    Published 2022
    “…Meanwhile, an improved parallel Jaya (IPJAYA) algorithm was proposed for searching the best parameters (C, Gama) values of SVM. …”
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    Thesis
  14. 14
  15. 15

    Intelligent classification algorithms in enhancing the performance of support vector machine by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…This paper presents two intelligent algorithms that hybridized between ant colony optimization (ACO) and SVM for tuning SVM parameters and selecting feature subset without having to discretize the continuous values. …”
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    Article
  16. 16

    Solving SVM model selection problem using ACOR and IACOR by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. …”
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    Article
  17. 17

    Intrusion Detection Systems, Issues, Challenges, and Needs by Aljanabi, Mohammad, Mohd Arfian, Ismail, Ali, Ahmed Hussein

    Published 2021
    “…However, these algorithms suffer from many lacks especially when apply to detect new type of attacks, and need for new algorithms such as JAYA algorithm, teaching learning-based optimization algorithm (TLBO) algorithm is arise. …”
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    Article
  18. 18

    Incremental continuous ant colony optimization for tuning support vector machine’s parameters by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2013
    “…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized.The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time.This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values.Eight datasets from UCI were used to evaluate the performance of the proposed algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset.…”
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    Article
  19. 19

    Using the bees algorithm to optimise a support vector machine for wood defect classification by Pham, D.T, Muhammad, Zaidi, Mahmuddin, Massudi, Ghanbarzadeh, Afshin, Koc, Ebubekir, Otri, Sameh

    Published 2007
    “…This paper describes a new application of the Bees Algorithm to the optimization of a Support Vector Machine (SVM) for the problem of classifying defects in plywood. …”
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    Conference or Workshop Item
  20. 20

    Crossover and mutation operators of real coded genetic algorithms for global optimization problems by Lim, Siew Mooi

    Published 2016
    “…This study is primarily aimed at investigating two issues in genetic algorithm (GA) and one issue in conformational search (CS) problems. …”
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    Thesis