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  1. 1
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  2. 2
    Exploring fruit fly evolutionary algorithm in a university examination timetabling environment

    Exploring fruit fly evolutionary algorithm in a university examination timetabling environment by Abdul Rahman, Syariza, Benjamin, Aida Mauziah, Ramli, Razamin, Ku-Mahamud, Ku Ruhana, Omar, Mohd Faizal

    Published 2019
    “…A new evolutionary algorithm namely the Fruit-Fly Optimization Algorithm (FOA) which is based on the behavior of finding food by the fruit fly is used as solution methodology. …”
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  3. 3
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  4. 4
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majid Khan bin Majahar Ali, Majid Khan bin Majahar Ali

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  5. 5
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  6. 6
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  7. 7
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. …”
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  8. 8
    Exploring dynamic self-adaptive populations in differential evolution

    Exploring dynamic self-adaptive populations in differential evolution by Teo, Jason Tze Wi

    Published 2006
    “…Although the Differential Evolution (DE) algorithm has been shown to be a simple yet powerful evolutionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand-tuning of the evolutionary parameters prior to commencing the actual optimization process. …”
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  9. 9
    Invigilation timetabling system using genetic algorithm (GA) / Shahirah Mohamed Hatim

    Invigilation timetabling system using genetic algorithm (GA) / Shahirah Mohamed Hatim by Shahirah, Mohamed Hatim

    Published 2011
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  10. 10
    Crossover-first differential evolution for improved global optimization in non-uniform search landscapes

    Crossover-first differential evolution for improved global optimization in non-uniform search landscapes by Teo, Jason Tze Wi, Mohd Hanafi Ahmad Hijazi, Hui, Keng Lau, Salmah Fattah, Aslina Baharum

    Published 2015
    “…The differential evolution (DE) algorithm is currently one of the most widely used evolutionary-based optimizers for global optimization due to its simplicity, robustness and efficiency. …”
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  11. 11
    A Practical Review on the Application of Constraint Handling Strategies in Evolutionary Computation from an Engineering Point of View

    A Practical Review on the Application of Constraint Handling Strategies in Evolutionary Computation from an Engineering Point of View by Yousefi, M., Hooshyar, D., Ahmad, R., Darus, A.N.

    Published 2015
    “…In this study, however, only the prominent methods and previous works are considered. The Evolutionary algorithms cannot handle the constraints by themselves, and the growing application of EAs in various fields of engineering and science, which are mostly highly constrained, has made the use of efficient, easyto- implement and comprehensive constraint handling strategies inevitable. …”
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  12. 12
    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
    “…DE is very sensitive to its parameter settings and mutation strategy; thus, this study aims to investigate these settings with the diverse versions of adaptive DE algorithms. This study has two main objectives: (1) to present an extension for the original taxonomy of evolutionary algorithms (EAs) parameter settings that has been overlooked by prior research and therefore minimize any confusion that might arise from the former taxonomy and (2) to investigate the various algorithmic design schemes that have been used in the different variants of adaptive DE and convey them in a new classification style. …”
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  13. 13
    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail

    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

    Published 2014
    “…The objective of this research is to estimate the Double Exponential Smoothing by using Genetic Algorithm Mechanism. The expected result of this research is Genetic Algorithm to able search for the best parameter in Double Exponential Smoothing.…”
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  14. 14
    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
    “…Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA). …”
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  15. 15
    Finding the root of nonlinear function using five bracketing method / Nur Afiqah Mohamed Azhar

    Finding the root of nonlinear function using five bracketing method / Nur Afiqah Mohamed Azhar by Mohamed Azhar, Nur Afiqah

    Published 2019
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  16. 16
    A practical review on the application of constraint handling strategies in evolutionary computation from an engineering point of view

    A practical review on the application of constraint handling strategies in evolutionary computation from an engineering point of view by Yousefi M., Hooshyar D., Ahmad R.B., Darus A.N.

    Published 2023
    “…In this study, however, only the prominent methods and previous works are considered. The Evolutionary algorithms cannot handle the constraints by themselves, and the growing application of EAs in various fields of engineering and science, which are mostly highly constrained, has made the use of efficient, easyto- implement and comprehensive constraint handling strategies inevitable. …”
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  17. 17
    Using genetic algorithms as image watermarking performance optimizer / Zuhaili Zahid

    Using genetic algorithms as image watermarking performance optimizer / Zuhaili Zahid by Zahid, Zuhaili

    Published 2008
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  18. 18
    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms

    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

    Published 2020
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  19. 19
    The solution of 2048 puzzle game using A* algorithm / Zafhira Ismail

    The solution of 2048 puzzle game using A* algorithm / Zafhira Ismail by Ismail, Zafhira

    Published 2017
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  20. 20
    Wind Farm Reactive Power Optimization by Using Imperialist Competitive Algorithm

    Wind Farm Reactive Power Optimization by Using Imperialist Competitive Algorithm by Soheilirad, M., Hizam, H., Farzan, P., Hojabri, Mojgan, Fallah, S. N., Soheilirad, G.

    Published 2013
    “…In this paper a new evolutionary computing method based on imperialist competitive algorithm (ICA) is used for optimization of the reactive power in a wind farm. …”
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