Search Results - (( evolution optimization learning algorithm ) OR ( parallel implementation a algorithm ))

Refine Results
  1. 1

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

    Published 2006
    “…Hybrid genetic algorithms are the combination of learning algorithms(Back propagation), usually working as evaluation functions, and genetic algorithms. …”
    Get full text
    Get full text
    Get full text
    Article
  2. 2

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…To overcome this problem, Differential Evolution (DE) has been used to determine optimal value for ANN parameters such as learning rate and momentum rate and also for weight optimization. …”
    Get full text
    Get full text
    Get full text
    Thesis
  3. 3
  4. 4

    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. …”
    Get full text
    Get full text
    Article
  5. 5

    An Efficient Parallel Quarter-sweep Point Iterative Algorithm for Solving Poisson Equation on SMP Parallel Computer by M., Othman, A. R., Abdullah

    Published 2000
    “…The experimental results of a test problem were compared with the later two parallel algorithms.…”
    Get full text
    Get full text
    Article
  6. 6

    Dual optimization approach in discrete Hopfield neural network by Guo, Yueling, Zamri, Nur Ezlin, Mohd Kasihmuddin, Mohd Shareduwan, Alway, Alyaa, Mansor, Mohd. Asyraf, Li, Jia, Zhang, Qianhong

    Published 2024
    “…Therefore, this research contributes to the improvement of the learning and retrieval phases by integrating the Hybrid Differential Evolution Algorithm and Swarm Mutation respectively. …”
    Get full text
    Get full text
    Article
  7. 7

    Parallelization of noise reduction algorithm for seismic data on a beowulf cluster by Aziz, I. A., Sandran, T., Haron, N. S., Hasan, M. H, Mehat, M.

    Published 2010
    “…This paper presents the parallelization of a sequential noise reduction algorithm for seismic data processing into a parallel algorithm. …”
    Get full text
    Get full text
    Citation Index Journal
  8. 8

    A permutation parallel algorithm under exchange restriction with message passing interface by Karim, Sharmila, Omar, Zurni, Ibrahim, Haslinda

    Published 2014
    “…The sequential algorithm is implemented to a parallel algorithm by integrating with Message Passing Interface (MPI) libraries by paralleling the starter sets generation task.The speedup and efficiency is the indicator tool for analyzing performance of this parallel algorithm.The results show reduction time computation of parallel algorithm among processors.…”
    Get full text
    Get full text
    Conference or Workshop Item
  9. 9

    Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development by Salehmin M.N.I., Tiong S.K., Mohamed H., Umar D.A., Yu K.L., Ong H.C., Nomanbhay S., Lim S.S.

    Published 2025
    “…This review uniquely focuses on harnessing the synergy between ML and computational modeling (CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction (HER) catalysts and various hydrogen production processes (HPPs). …”
    Review
  10. 10

    Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

    Published 2023
    “…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  11. 11

    Implementation of a parallel XTS encryption mode of operation by Alomari, Mohammad Ahmed, Samsudin, Khairulmizam, Ramli, Abdul Rahman

    Published 2014
    “…The parallel XTS algorithm has shown a speedup of 1.80, with 90% efficiency, faster than the sequential algorithm. …”
    Get full text
    Get full text
    Get full text
    Article
  12. 12

    Improved whale optimization algorithm for feature selection in Arabic sentiment analysis by Tubishat, Mohammad, Abushariah, Mohammad A.M., Idris, Norisma, Aljarah, Ibrahim

    Published 2019
    “…Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. …”
    Get full text
    Get full text
    Article
  13. 13
  14. 14

    Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

    Published 2011
    “…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  15. 15

    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

    Published 2014
    “…A data parallel algorithm (DPA-EHD) is designed and implemented for the EHD equations. …”
    Get full text
    Get full text
    Thesis
  16. 16

    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
    Get full text
    Get full text
    Get full text
    Article
  17. 17

    Parallel implementation on improved error signal of backpropagation algorithm by Mohd Aris, Teh Noranis

    Published 2001
    “…The main focus is to parallelise Shamsuddin's work in order to improve the speedup of the BP algorithm. The experiments are implemented using the Sequent Symmetry SE30 parallel machine. …”
    Get full text
    Get full text
    Thesis
  18. 18

    Parallel implementation on improved error signal of backpropagation algorithm by Mohd Aris, Teh Noranis, Sulaiman, Md. Nasir, Mohd Saman, Md. Yazid, Othman, Mohamed

    Published 2002
    “…The parallel version of the BP algorithm produces good speedup as the numbers of processors are increased and a 100% recognition rate for trained and untrained data is achieved.…”
    Get full text
    Get full text
    Get full text
    Article
  19. 19

    Performances Of Metaheuristic Algorithms In Optimizing Tool Capacity Allocations by Goheannee

    Published 2014
    “…In this research, the algorithms studied includes Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolution Algorithm, Harmony Search Algorithm, Teaching-LearningBased Optimization Algorithm and Black Hole Algorithm. …”
    Get full text
    Get full text
    Thesis
  20. 20

    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

    Published 2020
    “…To evaluate the PMT’s performance and adaptability, the PMT was applied to four contemporary metaheuristic algorithms, Differential Evolution, Particle Swarm Optimization, Simulated Annealing, and Whale Optimization Algorithm, to solve 15 well-known benchmark functions as well as 2 real world problems based on the welded beam design and pressure vessel design. …”
    Get full text
    Get full text
    Thesis