Hu, PeiPan, Jeng-ShyangChu, Shu-ChuanThe QUasi-Affine TRansformation Evolution (QUATRE) was first proposed by Meng et al. in 2016. It has the characteristics of few parameters and fast convergence. This paper brings two methods to improve its solution quality. The opposite position and comprehensive learning greatly advance the ability of jumping out of local traps when the QUATRE falls into stagnation. Their performance is verified by 23 benchmark functions. In the end, they succeed to train the parameters of neural network and predict the long-term traffic flow in Qingdao.


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    Title :

    Advanced QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm and Its Application for Neural Network


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:


    Publication date :

    2021-11-30


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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