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  1. 1
    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network

    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network by Taha, Imad, Inazy, Qabas

    Published 2006
    “…Genetic Algorithms are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. …”
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  2. 2
    Seed disperser ant algorithm for optimization / Chang Wen Liang

    Seed disperser ant algorithm for optimization / Chang Wen Liang by Chang , Wen Liang

    Published 2018
    “…This diversifies the search for global optimum. The classical benchmark problems and composite benchmark functions from Congress on Evolutionary Computation (CEC) 2005 special session is used for validate SDAA. …”
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  3. 3
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…DE has effectively solved various global optimization problems, including benchmark functions. …”
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  4. 4
    Crossover and mutation operators of real coded genetic algorithms for global optimization problems

    Crossover and mutation operators of real coded genetic algorithms for global optimization problems by Lim, Siew Mooi

    Published 2016
    “…This study is primarily aimed at investigating two issues in genetic algorithm (GA) and one issue in conformational search (CS) problems. …”
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  5. 5
    Evaluation of Search Result of Document Search Based GA (DSEGA)

    Evaluation of Search Result of Document Search Based GA (DSEGA) by Kamal Norfarid, Kamaruddin

    Published 2004
    “…Although i has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Genetic algorithms describe a set of optimization techniques that given a goal or fitness function are used to search a space for optimal points. …”
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  6. 6
    Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems

    Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems by Ahmad Azwan, Abd Razak, Ahmad Nor Kasruddin, Nasir

    Published 2023
    “…In this paper, the Manta Ray Foraging Optimization (MRFO) algorithm is applied to solve real parameter constrained optimization problems, using the Gradient-based Mutation MRFO (cMRFO) variant. …”
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  7. 7
    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy

    Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy by Al-Dabbagh, Rawaa Dawoud, Neri, Ferrante, Idris, Norisma, Baba, Mohd Sapiyan

    Published 2018
    “…The performance of most metaheuristic algorithms depends on parameters whose settings essentially serve as a key function in determining the quality of the solution and the efficiency of the search. …”
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  8. 8
    Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems

    Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir

    Published 2022
    “…GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. …”
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  9. 9
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…Apart from the best low-level heuristic, EMCQ also gives a chance to low-performing heuristics to search for the optimal threshold values using their probability density function. …”
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  10. 10
    Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed

    Selective harmonic elimination in cascaded H-bridge multilevel inverter using hybrid APSO algorithm / Mudasir Ahmed by Mudasir , Ahmed

    Published 2019
    “…In both algorithms, global searching is performed by APSO algorithm. …”
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  11. 11
    A hyper-heuristic based strategy for image segmentation using multilevel thresholding

    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…Apart from the best low-level heuristic, EMCQ also gives a chance to low-performing heuristics to search for the optimal threshold values using their probability density function. …”
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  12. 12
    Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices

    Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices by Vasant, P., Kose, U., Watada, J.

    Published 2017
    “…Mathematical modelling and parameter setting of TEC is combined with SA and DE to find better optimal findings. The work revealed that SA and DE can be applied successfully to solve single-objective and multi-objective TEC optimization problems. …”
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  13. 13
    A refined differential evolution algorithm for improving the performance of optimization process

    A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

    Published 2011
    “…Various Artificial Intelligent (AI) algorithms can be applied in solving optimization problems. …”
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  14. 14
    Broadening selection competitive constraint handling algorithm for faster convergence

    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    Published 2020
    “…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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  15. 15
    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Multi-objective optimization problems are also addressed by proposing a modified multi-criterion optimization algorithm based on a Pareto-based Particle Swarm Optimization (PSO) algorithm called Multi-Objective Particle Swarm Optimization (MOPSO). …”
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  16. 16
    Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses

    Hybrid differential evolution-particle swarm optimization algorithm for multi objective urban transit network design problem with homogeneous buses by Tarajo, Buba Ahmed, Lee, Lai Soon

    Published 2019
    “…This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. …”
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  17. 17
    Performance comparison of differential evolution and particle swarm optimization in constrained optimization

    Performance comparison of differential evolution and particle swarm optimization in constrained optimization by Iwan, Mahmud, Akmeliawati, Rini, Faisal, Tarig, Al-Assadi, Hayder M.A.A.

    Published 2012
    “…There are quite numbers of modern optimization algorithms proposed in the last two decades to solve optimization problems. …”
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  18. 18
    Hybrid Optimization Genetic Algorithms (HOGA) with Interactive Evolution to Solve Constraint Optimization Problems for Production Systems .

    Hybrid Optimization Genetic Algorithms (HOGA) with Interactive Evolution to Solve Constraint Optimization Problems for Production Systems . by Vasant, Pandian

    Published 2007
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  19. 19
    Reliably optimal PMU placement using disparity evolution-based genetic algorithm

    Reliably optimal PMU placement using disparity evolution-based genetic algorithm by Matsukawa, Yoshiaki, Othman, Mohammad Lutfi, Watanabe, Masayuki, Mitani, Yasunori

    Published 2017
    “…Optimal PMU Placement (OPP) problem as the combinatorial optimization problem has been formulated to determine the minimum PMU location in the power system. …”
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  20. 20
    Two level Differential Evolution algorithms for ARMA parameters estimatio

    Two level Differential Evolution algorithms for ARMA parameters estimatio by Salami, Momoh Jimoh Emiyoka, Tijani, Ismaila, Aibinu, Abiodun Musa

    Published 2013
    “…An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. …”
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