A genetic algorithm based double layer neural network for solving quadratic bilevel programming problem

Jingru Li, Junzo Watada, Shamshul Bahar Yaakob

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    In this paper, an intelligent genetic algorithm (IGA) and a double layer neural network (NN) are integrated into a hybrid intelligent algorithm for solving the quadratic bilevel programming problem. The intelligent genetic algorithm is used to select a set of potential solution combinations from the entire generated combinations of the upper level. Then a meta-controlled Boltzmann machine, which is formulated by comprising the Hopfield model (HM) and the Boltzmann machine (BM), is used to effectively and efficiently determine the optimal solution of the lower level. Numerical experiments on examples show that the genetic algorithm based double layer neural network enables us to efficiently and effectively solve quadratic bilevel programming problems.

    本文言語English
    ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ382-389
    ページ数8
    ISBN(印刷版)9781479914845
    DOI
    出版ステータスPublished - 2014 9 3
    イベント2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing
    継続期間: 2014 7 62014 7 11

    Other

    Other2014 International Joint Conference on Neural Networks, IJCNN 2014
    CityBeijing
    Period14/7/614/7/11

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

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