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  1. 1
    Simultaneous Adaptation Of Multiple Genetic Algorithm Parameters Using Fuzzy Logic Controllers

    Simultaneous Adaptation Of Multiple Genetic Algorithm Parameters Using Fuzzy Logic Controllers by Ghallab, Abdullatif Saleh Nasser

    Published 2010
    “…This study aims at designing an online adaptive method to control multiple parameters of the Genetic Algorithm. …”
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  2. 2
    Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system

    Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system by Krishnan P.S., Kiong T.S., Koh J., Yap D.

    Published 2023
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  3. 3
    A comparative study for parameter selection in online auctions

    A comparative study for parameter selection in online auctions by Gan, Kim Soon

    Published 2009
    “…The bidding strategy from the experimental result of this experiment will eventually perform better than the bidding strategy that applied fixed static genetic operator's probabilities. Self adaptation genetic algorithm is the last model that will be used to evolve the bidding strategy. …”
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  4. 4
    Adaptive Impedance Tuning Network Using Genetic Algorithm: ITUNEGA

    Adaptive Impedance Tuning Network Using Genetic Algorithm: ITUNEGA by Wong, Yan Chiew

    Published 2016
    “…Adaptive impedance tuning algorithms are used to preserve the link quality of mobile phones under fluctuating user conditions.It is highly desirable to correct the complex impedance mismatch with high convergence rate.Presented here, is a novel technique for correcting impedance mismatch in adaptive impedance tuning network by exploiting the relationships among the genetic algorithm’s coefficient values derived from the matching network parameters. …”
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  5. 5
    Workability review of genetic algorithm approach in networks

    Workability review of genetic algorithm approach in networks by Nurika, O., Zakaria, N., Hassan, F., Jung, L.T.

    Published 2014
    “…Generally, genetic algorithm process will accomplish according to its parameters sizes. …”
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  6. 6
    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fed-batch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof

    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fed-batch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yuso... by Mamat, Kamaruddin, Mohd Yusof, Farida Zuraina

    Published 2008
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  7. 7
    A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems.

    A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems. by Lutfy, Omar Farouq, Mohd Noor, Samsul Bahari, Marhaban, Mohammad Hamiruce, Ali Abbas, Kassim

    Published 2009
    “…This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. …”
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  8. 8
    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fedbatch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof

    Application of genetic algorithms to model parameter identification of a recombinant e.coli high-cell density fedbatch fermentation / Kamaruddin Mamat and Farida Zuraina Mohd Yusof by Mamat, Kamaruddin, Mohd Yusof, Farida Zuraina

    Published 2008
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  9. 9
    Attack path selection optimization with adaptive genetic algorithms

    Attack path selection optimization with adaptive genetic algorithms by Abd Rahman, A.S., Zakaria, M.N., Masrom, S.

    Published 2016
    “…We describe our approach for implementing an optimized security assessment using Genetic Algorithm (GA). Because of the dynamic nature of an enterprise network, all security analysis tools devised for the network need to function in dynamic mode as well. …”
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  10. 10
    Attack path selection optimization with adaptive genetic algorithms

    Attack path selection optimization with adaptive genetic algorithms by Abd Rahman, A.S., Zakaria, M.N., Masrom, S.

    Published 2016
    “…We describe our approach for implementing an optimized security assessment using Genetic Algorithm (GA). Because of the dynamic nature of an enterprise network, all security analysis tools devised for the network need to function in dynamic mode as well. …”
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  11. 11
    Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining

    Sensitivity analysis of drill wear and optimization using Adaptive Neuro fuzzy –genetic algorithm technique toward sustainable machining by Saw, Lip Huat, Ho, Li Wen, Yew, Ming Chian, Yusof, Farazila, Pambudi, Nugroho Agung, Ng, Tan Ching, Yew, Ming Kun

    Published 2018
    “…Effects of spindle rotational speed, feed rate and diameter of drill on tool wear were determined through Adaptive Neuro Fuzzy Inference System (ANFIS). Next, genetic algorithm (GA) was used to identify the optimal drilling parameter for different diameters of drill. …”
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  12. 12
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…However, the MGCACO algorithm has three main drawbacks in producing a features subset because of its clustering method, parameter sensitivity, and the final subset determination. …”
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  13. 13
    Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction

    Enhancing hyperparameters of LSTM network models through genetic algorithm for virtual learning environment prediction by Ismanto, Edi, Ab Ghani, Hadhrami, Md Saleh, Nurul Izrin

    Published 2025
    “…These findings highlight the effectiveness of adaptive and genetic algorithms in enhancing LSTM model performance for VLE prediction, offering valuable insights for educational technology advancement.…”
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  14. 14
    Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy

    Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy by Ehteram M., Ahmed A.N., Fai C.M., Afan H.A., El-Shafie A.

    Published 2023
    “…Air quality; Decision making; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Inference engines; Mapping; Mean square error; Multiobjective optimization; Optimal systems; Particle swarm optimization (PSO); Quality control; Renewable energy resources; Solar radiation; Uncertainty analysis; Adaptive neuro-fuzzy inference system; ANFIS; Multi objective algorithm; Multi objective particle swarm optimization; Multi-objective genetic algorithm; Renewable energies; Renewable energy generation; Solar radiation forecasting; Parameter estimation…”
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  15. 15
    Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA by Zahari, Taha, Farzad, Tahriri, Siti Zawiah, Md Dawal

    Published 2014
    “…Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. …”
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  16. 16
    Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

    Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP by Kakudi, Habeebah Adamu, Loo, Chu Kiong, Moy, Foong Ming, Masuyama, Naoki, Pasupa, Kitsuchart

    Published 2019
    “…We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. …”
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  17. 17
    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
    “…Consequently, some components of GAs had been modified to produce Vector Evaluated Genetic Algorithm (VEGA) in order to adapt the nature of MOOP. …”
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  18. 18
    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…To overcome these ANN problems, the Genetic Algorithm (GA) has been most frequently used for this purpose, however, some drawbacks of GA include, slow search speed and dependence on initial parameters. …”
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  19. 19
    Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil

    Web based personalized university timetable for UiTM students using genetic algorithm / Mohd Radhi Fauzan Jamli and Ahmad Firdaus Ahmad Fadzil by Jamli, Mohd Radhi Fauzan, Ahmad Fadzil, Ahmad Firdaus

    Published 2024
    “…Extensive experimentation with genetic algorithm parameters has yielded promising results, notably a parameter set (population size = 12, generation size = 30, mutation rate = 0.2) demonstrating robust performance, achieving optimal timetables with swift convergence and minimal conflicts. …”
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  20. 20
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…Not many researchers have investigated the effects of optimizing both the topology structures and the parameters used in ANNs. This research utilizes a genetic algorithm (GA) to optimize the multi-layer FFNN performance and structure in modelling three datasets: network traffic, rainfall, and tourist. …”
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