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  1. 161

    Simulated evolution for timing and low power VLSI standard cell placement by Sait, Sadiq M.

    Published 2020
    “…Comparison with genetic algorithm is discussed. r 2003 Elsevier Ltd. …”
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    article
  2. 162

    Scheduling and allocation in high-level synthesis using stochastic techniques by Sait, Sadiq M.

    Published 2020
    “…Scheduling and allocation can be formulated as an optimization problem. In this work, a unique approach to scheduling and allocation problem using the genetic algorithm (GA) is described. …”
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    article
  3. 163

    Data Generation for Path Testing by Mansour, Nashat

    Published 2004
    “…The two algorithms are: a simulated annealing algorithm (SA), and a genetic algorithm (GA). …”
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    article
  4. 164

    DSCE: Comparative Analysis of Heuristic Computational Techniques by Bany Muhammad, Nooh

    Published 2021
    “…Various HCTs are also compared, including genetic algorithms (GAs), particle swarm optimization (PSO), differential evolution (DE), cat swarm optimization (CSO), bee colony optimization (BCO), hybrid GA-PSO (HGP), and the cuckoo search algorithm (CSA). …”
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  5. 165
  6. 166

    Finite state machine state assignment for area and power minimization by El-Maleh, A.

    Published 2006
    “…A fuzzy-based aggregation function is employed to combine the two objectives. The work employs genetic algorithm for search space exploration. Experimental results demonstrate the effectiveness of the proposed measures.…”
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    article
  7. 167

    Localization of Damages in Plain And Riveted Aluminium Specimens using Lamb Waves by S. Andhale, Yogesh

    Published 2018
    “…The genetic optimization (GO) method is used to further refine the location of damage within the enclosed area obtained using astroid algorithm. …”
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  8. 168

    Impacts of On-Grid Solar PV on Distribution Networks and Potential Solutions: A Case Study in the Region of Zahle by Korkmaz, Jessica

    Published 2025
    “…The siting and sizing methodology is conducted by considering five different optimization algorithms, namely the single-objective genetic algorithm (SOGA), the combined SOGA and loss sensitivity factor algorithm (SOGA-LSF), the multi-objective genetic algorithm (MOGA), the combined MOGA-LSF and the CAPADD algorithm of OpenDSS. …”
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  9. 169

    Graph Contraction for Mapping Data on Parallel Computers by Mansour, N.

    Published 1994
    “…We then present experimental results on using contracted graphs as inputs to two physical optimization methods; namely, genetic algorithm and simulated annealing. …”
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  10. 170

    A simulated evolution approach to task-matching and scheduling in heterogeneous computing environments by Barada, Hassan

    Published 2020
    “…The performance of SE is compared with a genetic algorithm approach for the same problem with respect to the quality of solutions generated, and timing requirements of the algorithms. r 2003 Elsevier Science Ltd. …”
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    article
  11. 171

    Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives by Nasir Uddin, M.

    Published 2004
    “…At each operating condition a genetic algorithm is used to optimize the PI controller parameters in a closed-loop vector control scheme. …”
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    article
  12. 172

    Enhancement of Frequency Control for Stand-Alone Multi-Microgrids by Kavita Singh (182141)

    Published 2021
    “…For getting superior outcomes and enhanced steadiness of the microgrid, the controller gains are streamlined utilizing an imperialist competitive algorithm (ICA). To demonstrate the efficiency of ICA, The obtained results are compared with the genetic algorithm and particle swarm optimization algorithm. …”
  13. 173

    Test bus assignment, sizing, and partitioning for system-on-chip by Harmanani, Haidar M.

    Published 2007
    “…In this paper, an efficient genetic algorithm for designing test access architectures while investigating test bus sizing and concurrently assigning cores to test buses is proposed. …”
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  14. 174

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

    Published 2004
    “…Then, a real-coded genetic algorithm is employed to search for optimal controller parameters. …”
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    article
  15. 175

    Cost-Benefit Analysis of Genotype-Guided Interruption Days in Warfarin Pre-Procedural Management by Islam Eljilany (14151588)

    Published 2023
    “…As per 10.3% prevalence of genetic variants, 82% bridging, and a calculated 20% optimization in the preparative period of warfarin management, the benefit to cost ratio was 4.0 in favor genotype-guided approach. …”
  16. 176

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

    Published 2005
    “…Then, a real-coded genetic algorithm (RCGA) is employed to search for optimal stabilizer parameters. …”
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    article
  17. 177

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

    Published 2003
    “…The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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    article
  18. 178

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

    Published 2003
    “…The real-coded genetic algorithm (RCGA) is employed to search for optimal controller parameters. …”
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    article
  19. 179

    A new bi-objective model of the urban public transportation hub network design under uncertainty by Firoozeh Kaveh (14150877)

    Published 2019
    “…Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. …”
  20. 180

    Inferential sensing techniques in industrial applications by Shakil,, Muhammad

    Published 0007
    “…System delays are obtained by approximating the model by a linear model. Genetic algorithm, which is a heuristic optimization technique, is used to ¯nd the system delays of the linear model, which are used in dynamical neural network model. …”
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    masterThesis