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  1. 1
    A Method For Solving Mult-Objective Optimization Problem: Vector Evaluated Genetic Algorithm (Vega)

    A Method For Solving Mult-Objective Optimization Problem: Vector Evaluated Genetic Algorithm (Vega) by Tan, Tun Tai

    Published 2009
    “…However, it is impractical to solve MOOP by using classical methods due to its complexity. Genetic Algorithms (GAs) are a powerful stochastic search in solving optimization problems. …”
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    Final Year Project Report / IMRAD
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  2. 2
    Mobile robot path planning using hybrid genetic algorithm and traversability vectors method

    Mobile robot path planning using hybrid genetic algorithm and traversability vectors method by Loo, C.K., Rajeswari, M., Wong, E.K., RaoTask, M.V.C.

    Published 2004
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  3. 3
    Extending the decomposition algorithm for support vector machines training

    Extending the decomposition algorithm for support vector machines training by Zaki, N,M., Deris, S., Chin, K.K.

    Published 2003
    “…Using a conventional optimizer to train SVM is not the ideal solution. …”
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  4. 4
    A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

    A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming by Darzi S., Sieh Kiong T., Tariqul Islam M., Rezai Soleymanpour H., Kibria S.

    Published 2023
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  5. 5
    Formulating new enhanced pattern classification algorithms based on ACO-SVM

    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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  6. 6
    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms

    Optimization Of Two-Dimensional Dual Beam Scanning System Using Genetic Algorithms by Koh, Johnny Siaw Paw

    Published 2008
    “…This thesis presents a new approach to optimize the performance of a dual beam optical scanning system in terms of its scanning combinations and speed, using Genetic Algorithm (GA). …”
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    Thesis
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  7. 7
    Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems

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

    Published 2022
    “…Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. …”
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    Thesis
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  8. 8
    Real-Time Optimal Trajectory Correction (ROTC) for autonomous quadrotor / Noorfadzli Abdul Razak

    Real-Time Optimal Trajectory Correction (ROTC) for autonomous quadrotor / Noorfadzli Abdul Razak by Abdul Razak, Noorfadzli

    Published 2018
    “…The scheme performance is evaluated according to its accuracy to compute the vector compared to estimation method using Laser Range Finder (LRF) modules. …”
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    Thesis
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  9. 9
    Feedforward neural network for solving particular fractional differential equations

    Feedforward neural network for solving particular fractional differential equations by Admon, Mohd Rashid

    Published 2024
    “…This research aims to develop a scheme based on a feedforward neural network (FNN) with a vectorized algorithm (FNNVA) for solving FDEs in the Caputo sense (FDEsC) using selected first-order optimization techniques: simple gradient descent (GD), momentum method (MM), and adaptive moment estimation method (Adam). …”
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  10. 10
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  11. 11
    Application of Optimization Methods for Solving Clustering and Classification Problems

    Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

    Published 2011
    “…Clustering problem is discussed as a problem of non-smooth, non-convex optimization and a new method for solving this optimization problem is developed. …”
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  12. 12
    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset

    Auto-feed hyperparameter support vector regression prediction algorithm in handling missing values in oil and gas dataset by Amirruddin, A., Aziz, I.A., Hasan, M.H.

    Published 2020
    “…This problem inspires the idea to develop a prediction algorithm to predict the missing values in the dataset, where Support vector regression (SVR) has been proposed as a prediction method to predict missing values in several academic types of researches. …”
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  13. 13
    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem

    A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem by Mohd Pozi, Muhammad Syafiq

    Published 2016
    “…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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  14. 14
    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui

    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

    Published 2019
    “…One of the major research problems is the computation resources required by machine learning algorithm used for classification for HAR. …”
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  15. 15
    Improved intrusion detection algorithm based on TLBO and GA algorithms

    Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

    Published 2021
    “…Optimization algorithms are widely used for the identification of intrusion. …”
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  16. 16
    Enhancement of new smooth support vector machines for classification problems

    Enhancement of new smooth support vector machines for classification problems by Santi Wulan, Purnami

    Published 2011
    “…To obtain optimal accuracy results, Uniform Design method is used to select parameter. …”
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  17. 17
    Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization

    Representation Of Rational Bézier Quadratics Using Genetic Algorithm, Differential Evolution And Particle Swarm Optimization by Yahya, Zainor Ridzuan

    Published 2013
    “…Alternatively, artificial intelligence techniques are widely used for optimization problem with shorter computation time. …”
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  18. 18
    Optimal null steering of minimum variance distortionless response adaptive beamforming using particle swarm optimization and gravitational search algorithm

    Optimal null steering of minimum variance distortionless response adaptive beamforming using particle swarm optimization and gravitational search algorithm by Darzi S., Tiong S.K., Islam M.T., Ismail M., Kibria S.

    Published 2023
    Conference Paper
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  19. 19
    Enhancing Classification Algorithms with Metaheuristic Technique

    Enhancing Classification Algorithms with Metaheuristic Technique by Cokro, Nurwinto, Tri Basuki, Kurniawan, Misinem, ., Tata, Sutabri, Yesi Novaria, Kunang

    Published 2024
    “…Meta-heuristic algorithms are search techniques used to solve complexoptimization problems, and these algorithms can help provide reasonable solutions in a shorter time thanexact methods. …”
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  20. 20
    Quasi-Newton methods based on ordinary differential equation approach for unconstrained nonlinear optimization

    Quasi-Newton methods based on ordinary differential equation approach for unconstrained nonlinear optimization by Khiyabani, Farzin Modarres, Leong, Wah June

    Published 2014
    “…Fundamentally this is a gradient system based on the first order optimality conditions of the optimization problem. To further extend the methods for solving large-scale problems, a feature incorporated to the proposed methods is that a limited memory setting for matrix–vector multiplications is used, thus avoiding the computational and storage issues when computing Jacobian information. …”
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