Showing 1 - 20 results of 34 for search '(codes OR (code OR mode)) genetic algorithm', query time: 0.09s Refine Results
  1. 1

    A New Parallel Genetic Algorithm Model by Al-Somani, Turki F.

    Published 2020
    “…This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. …”
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    article
  2. 2

    A new genetic algorithm approach for unit commitment by Mantawy, A.H.

    Published 1997
    “…This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. …”
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    article
  3. 3

    Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

    Published 2016
    “…Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. …”
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  4. 4

    Genetic Algorithm Based Simultaneous Eigenvalue Placement Of Power Systems by Abdel Magid, Y.L.

    Published 2020
    “…The task of selecting the output feedback gains is converted to a simple optimization problem with an eigenvaluebased objective function, which is solved by a genetic algorithm. An objective function is presented allowing the selection of the output feedback gains to place the closed-loop eigenvalues in the left-hand side of a vertical line in the complex s-plane while shifting a specific mode of oscillation to a vertical strip and with bounds on the damping ratio. …”
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    article
  5. 5

    A genetic-based algorithm for fuzzy unit commitment model by Mantawy, A.H.

    Published 2000
    “…The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. The genetic algorithm (GA) approach is then used to solve the proposed fuzzy UCP model. …”
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    article
  6. 6

    Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach by Sarif, Bambang

    Published 2006
    “…The search space is too large to be explored by deterministic algorithms. In this paper, a Genetic Algorithm based algorithm for synthesis of MVL functions is proposed. …”
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    article
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    Design of PSS and STATCOM-based damping stabilizers using genetic algorithms by Abido, M.A.

    Published 2006
    “…The design problem of STATCOM-based stabilizers is formulated as an optimization problem. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. …”
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    article
  9. 9

    Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem by Mantawy, A.H.

    Published 1999
    “…A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. …”
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    article
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    Robust Coordinated Design of Excitation and TCSC-Based Stabilizers Using genetic algorithms by Abdel-Magid, Y. L.

    Published 2004
    “…The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with an eigenvalue-based objective function. The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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    article
  12. 12

    A New Genetic-Based Tabu Search Algorithm For Unit Commitment Problem by Mantawy, A. H.

    Published 2020
    “…In the proposed algorithm, genetic algorithm solution is coded as a mix between binary and decimal representation. …”
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    article
  13. 13

    Integrating Genetic Algorithms, Tabu Search, And Simulated Annealing For The Unit Commitment Problem by Montawy, A.H.

    Published 2020
    “…A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. …”
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    article
  14. 14

    Optimal multiobjective design of robust power system stabilizers using genetic algorithms by Abdel-Magid, Y.L.

    Published 2003
    “…A multiobjective problem is formulated to optimize a composite set of objective functions comprising the damping factor, and the damping ratio of the lightly damped electromechanical modes. The problem of robustly selecting the parameters of the power system stabilizers is converted to an optimization problem which is solved by a genetic algorithm with the eigenvalue-based multiobjective function. …”
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    article
  15. 15

    Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

    Published 2023
    “…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V<sub>pred</sub>/V<sub>exp</sub>) ratio of 0.97 and a coefficient of variation of 17.91%.…”
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    A genetic-based fuzzy logic power system stabilizer formultimachine power systems by Abido, M.A.

    Published 1997
    “…This paper presents a novel approach to combine genetic algorithms (GA) with fuzzy logic systems to design a genetic-based fuzzy logic power system stabilizer (GFLPSS) for multimachine power systems. …”
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    article
  19. 19

    Simultaneous stabilization of multimachine power systems viagenetic algorithms by Abdel-Magid, Y.L.

    Published 1999
    “…This paper demonstrates the use of genetic algorithms for the simultaneous stabilization of multimachine power systems over a wide range of operating conditions via single-setting power system stabilizers. …”
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    article
  20. 20

    Comprehensive whole genome sequence analyses yields novel genetic and structural insights for Intellectual Disability by Farah R. Zahir (18892108)

    Published 2017
    “…The <i>de novo</i> assembly resulted in unmasking hidden genome instability that was missed by standard re-alignment based algorithms. We also interrogated regulatory sequence variation for known and hypothesized ID genes and present useful strategies for WGS data analyses for non-coding variation.…”