### Abstract

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.

Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 382-389 |

Number of pages | 8 |

ISBN (Print) | 9781479914845 |

DOIs | |

Publication status | Published - 2014 Sep 3 |

Event | 2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing Duration: 2014 Jul 6 → 2014 Jul 11 |

### Other

Other | 2014 International Joint Conference on Neural Networks, IJCNN 2014 |
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City | Beijing |

Period | 14/7/6 → 14/7/11 |

### Fingerprint

### ASJC Scopus subject areas

- Software
- Artificial Intelligence

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(pp. 382-389). [6889483] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889483

**A genetic algorithm based double layer neural network for solving quadratic bilevel programming problem.** / Li, Jingru; Watada, Junzo; Yaakob, Shamshul Bahar.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the International Joint Conference on Neural Networks.*, 6889483, Institute of Electrical and Electronics Engineers Inc., pp. 382-389, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, 14/7/6. https://doi.org/10.1109/IJCNN.2014.6889483

}

TY - GEN

T1 - A genetic algorithm based double layer neural network for solving quadratic bilevel programming problem

AU - Li, Jingru

AU - Watada, Junzo

AU - Yaakob, Shamshul Bahar

PY - 2014/9/3

Y1 - 2014/9/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84908470045&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908470045&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2014.6889483

DO - 10.1109/IJCNN.2014.6889483

M3 - Conference contribution

AN - SCOPUS:84908470045

SN - 9781479914845

SP - 382

EP - 389

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

ER -