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  1. 1

    A genetic algorithm for capital budgeting problem with fuzzy parameters by Rashidi-Bajgan, Hannaneh, Rezaeian, Javad, Nehzati, Taravatsadat, Ismail, Napsiah

    Published 2010
    “…Thereupon, a fuzzy simulation-based genetic algorithm is provided for solving chance constrained integer programming model with fuzzy parameters.…”
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    Conference or Workshop Item
  2. 2

    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

    Published 2022
    “…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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    Thesis
  3. 3

    Explicit solution of parameter estimate using multiparametric programming for boost converter by Mid, E.C., Mukhtar, N.M., Syed Yunus, S.H., Abdul Hadi, Dayanasari, Ruslan, Eliyana

    Published 2023
    “…A multiparametric programming (MPP) algorithm is fundamental to the suggested methodology, which aims to develop parameters as explicit functions of measurements. …”
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  4. 4
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    Parameter estimation of multivariable system using Fuzzy State Space Algorithm / Razidah Ismail … [et al.] by Ismail, Razidah, Ahmad, Tahir, Harish, Noor Ainy, A. Halim, Rosenah

    Published 2011
    “…The main feature of the model is the development of the Fuzzy State Space Algorithm (FSSA) for determination of input parameters that can be applied to any multivariable dynamic system. …”
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    Research Reports
  6. 6

    Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems by Ismail, Razidah

    Published 2005
    “…This model is used for optimization of input parameters in multivariable dynamic systems. …”
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    Thesis
  7. 7

    Formulating and solving stochastic truck and trailer routing problems using meta-heuristic algorithms / Seyedmehdi Mirmohammadsadeghi by Seyedmehdi, Mirmohammadsadeghi

    Published 2015
    “…The solutions have been significantly improved by the algorithms. This issue has been formulated under chance constrained programming (CCP) model and stochastic programming model with recourse (SPR). …”
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    Thesis
  8. 8

    Controller And Circuitry Design And Implementation For Fire Fighting Robot For MIROC 2014 Competition by Reyfill Konnik, Charlie Ivan

    Published 2014
    “…This algorithm is designed using an iterative design model, whereby the program is continually tested and improved. …”
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    Final Year Project
  9. 9

    Quality of service in mobile IP networks with parametric multi-channel routing algorithms based on linear programming approach by Gholizadeh, Somayyeh

    Published 2018
    “…This approach tunes the parameters of the linear programming models that are used in the other algorithms by using a dynamic element. …”
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    Thesis
  10. 10

    Software for modelling the static and dynamic flux linkage - current characteristics of A 6/4 SRM by Hashim, Mohd Nasir

    Published 1996
    “…Program in C language was developed to validate the algorithm provided by Torrey [5,7].…”
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    Student Project
  11. 11

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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    Article
  12. 12

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…One of the progressing works for automated machine learning improvement is the inclusion of evolutionary algorithm such as Genetic Programming. The function of Genetic Programming is to optimize the best combination of solutions from the possible pipelines of machine learning modelling, including selection of algorithms and parameters optimization of the selected algorithm. …”
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    Article
  13. 13

    Novice programmers’ emotion and competency assessments using machine learning on physiological data / Fatima Jannat by Fatima, Jannat

    Published 2022
    “…The Long Short-term Memory (LSTM) deep learning model was chosen for classifying programming learners according to their performance. …”
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    Thesis
  14. 14
  15. 15

    Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment by Kalaf, Kalaf, Bayda Atiya

    Published 2017
    “…For these proposed approaches, this study adopted a hybridization of a fuzzy programming, modify simulated annealing, and simplex downhill (SD) algorithm called Fuzzy-MSASD to resolve multiple objective linear programming APP problems in a fuzzy environment. …”
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    Thesis
  16. 16

    Modelling and optimisation of blood glucose control for type 1 diabetes using multi-parametric programming and model-based predictive control (mp-MPC) / Associate Professor Dr Ayub... by Md. Som, Ayub

    Published 2014
    “…In doing so, Multi-Parametric Programming technique is used to develop the computer algorithm; whereas Model-Based Predictive Control (MPC) is adopted for the design of the controller. …”
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    Monograph
  17. 17

    Self-similar network traffic using Random Midpoint Displacement (RMD) algorithm / Jumaliah Saarini by Saarini, Jumaliah

    Published 2006
    “…The way to solve this problem, we applied the existed method in visual C++ programming with used the Random midpoint Displacement (RMD) algorithm. …”
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    Student Project
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    Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches by Ong, Pauline, Vui, Desmond Daniel Sheng Chin, Choon, Sin Ho, Chuan, Huat Ng

    Published 2018
    “…The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. …”
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    Article
  20. 20

    3D coordinate transformation using molodensky badekas transformation model: MBT07 by Phang, Seng Boon, Setan, Halim

    Published 2007
    “…Another short program was developed with MATLAB, to confirm the algorithms involved in Molodensky Badekas transformation before creating MBT07. …”
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    Conference or Workshop Item