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  1. 1

    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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  2. 2

    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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  3. 3

    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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  4. 4

    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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  5. 5

    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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  6. 6
  7. 7

    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
  8. 8

    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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  9. 9

    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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  10. 10

    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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  12. 12

    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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  13. 13
  14. 14

    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.…”
  15. 15

    Dynamic single node failure recovery in distributed storage systems by Itani, M.

    Published 2017
    “…To minimize the system repair cost, we formulate our problem using incidence matrices and solve it heuristically using genetic algorithms for all possible cases of single node failures. …”
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  16. 16

    Analysis of power system stability enhancement via excitation and FACTS-based stabilizers by Abido, M. A.

    Published 2004
    “…An eigenvalue-based objective function to increase the system damping and improve the system response is developed. Then, a real-coded genetic algorithm is employed to search for optimal controller parameters. …”
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  17. 17

    Power system stability enhancement via coordinated design of a PSS and an SVC-based controller by Abido, M.A.

    Published 2003
    “…The coordinated design problem of robust excitation and SVC-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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  18. 18

    Coordinated design of robust excitation and TCSC-based damping controllers by Abido, M.A.

    Published 2003
    “…The coordinated design problem of robust excitation and TCSC-based controllers over a wide range of loading conditions and system configuration 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
  19. 19

    Analysis and Assessment of STATCOM-Based Damping Stabilizers for Power System Stability Enhancement by Abido, M. A.

    Published 2005
    “…For coordination purposes, a time domain-based multiobjective junction to improve the system stability as well as ac and dc voltage regulation is proposed. Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. …”
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  20. 20

    Practical Multiple Node Failure Recovery in Distributed Storage Systems by Itani, M.

    Published 2016
    “…The problem is solved using genetic algorithms that search within the feasible solution space. …”
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