Search Results - (( _ implication force algorithm ) OR ( evolution classification parallel algorithm ))

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    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.…”
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    A new inexact line search method for convex optimization problems by Moyi, Aliyu Usman, Leong, Wah June

    Published 2013
    “…In general one can say that line search procedure for the steplength and search direction are two important elements of a line search algorithm. The line search procedure requires much attention because of its far implications on the robustness and efficiency of the algorithm. …”
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    The relationships between brand attributes and word of mouth on brand identity and brand image by Al Kasassbh, Hazem Mohamad Abd Al Ghany

    Published 2017
    “…Finally, the study's implications for theory and practice, limitations, conclusions as well as directions for future research are provided and discussed.…”
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