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  1. 1
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  2. 2
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  3. 3
    The effect of adaptive parameters on the performance of back propagation

    The effect of adaptive parameters on the performance of back propagation by Abdul Hamid, Norhamreeza

    Published 2012
    “…The activation functions are adjusted by the adaptation of gain parameters together with adaptive momentum and learning rate value during the learning process. …”
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  4. 4
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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  5. 5
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  6. 6
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  7. 7
    Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates

    Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates by Yeong, Lin Koay, Hong, Seng Sim, Yong, Kheng Goh, Sing, Yee Chua, Wah, June Leong

    Published 2024
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  8. 8
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  9. 9
    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

    Published 2019
    “…This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). …”
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  10. 10
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  11. 11
    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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  12. 12
    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. …”
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  13. 13
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
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  14. 14
    A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites

    A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

    Published 2019
    “…Unlike most existing meta-heuristic algorithms, and by virtue of being parameter-free, TLBO does not have any specific parameter controls. …”
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  15. 15
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…Many algorithms have been implemented to solve the grid scheduling problem. …”
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  16. 16
    Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms

    Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms by Mailah, Musa, Ong, Miaw Yong

    Published 2001
    “…These parameters are adaptively computed on-line while the robot is executing a trajectory tracking task and subject to some forms of external disturbances. …”
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  17. 17
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

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

    Published 2019
    “…For its fast convergence and for its efficient search procedure, the self-adaptation is proposed in the parameters of the proposed hybrid algorithm. …”
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  18. 18
    Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi

    Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi by Jabbar Hayyawi, Mustafa

    Published 2016
    “…Design and development an adaptive learning algorithm controller ALAC of position the actuators is presented in real time parallel manipulator based on artificial neural network ANN……”
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  19. 19
    Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle

    Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

    Published 2023
    “…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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  20. 20
    Nature-inspired parameter controllers for ACO-based reactive search

    Nature-inspired parameter controllers for ACO-based reactive search by Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani

    Published 2015
    “…This study proposes machine learning strategies to control the parameter adaptation in ant colony optimization algorithm, the prominent swarm intelligence metaheuristic.The sensitivity to parameters’ selection is one of the main limitations within the swarm intelligence algorithms when solving combinatorial problems.These parameters are often tuned manually by algorithm experts to a set that seems to work well for the problem under study, a standard set from the literature or using off-line parameter tuning procedures. …”
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