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  • Designing evolutionary feedforward neural networks using social spider optimization algorithm

    Author(s)
    Mirjalili, Seyedeh Zahra
    Saremi, Shahrzad
    Mirjalili, Seyed Mohammad
    Griffith University Author(s)
    Saremi, Shahrzad
    Year published
    2015
    Metadata
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    Abstract
    Training feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified ...
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    Training feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified by the comparison with five other well-known heuristics. The results prove that the proposed FNNSSO is able to provide very promising results compared with other algorithms.
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    Journal Title
    Neural Computing and Applications
    Volume
    26
    Issue
    8
    DOI
    https://doi.org/10.1007/s00521-015-1847-6
    Subject
    Artificial intelligence not elsewhere classified
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/101357
    Collection
    • Journal articles

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