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  • How effective is the Grey Wolf optimizer in training multi-layer perceptrons

    Author(s)
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2015
    Metadata
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    Abstract
    This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very ...
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    This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer.
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    Journal Title
    Applied Intelligence
    Volume
    43
    DOI
    https://doi.org/10.1007/s10489-014-0645-7
    Subject
    Neural, Evolutionary and Fuzzy Computation
    Artificial Intelligence and Image Processing
    Publication URI
    http://hdl.handle.net/10072/69261
    Collection
    • Journal articles

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