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A neurocomputing model for real coded genetic algorithm with the minimal generation gap

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Abstract

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA.

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Acknowledgement

This work was supported by National Natural Science Foundation of China under Grant 60274060 and 60304012.

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Authors and Affiliations

  1. School of Electronic Information and Control Engineering, Beijing University of Technology, 100022, Beijing, P.R. China

    D.-X. Gong, X.-G. Ruan & J.-F. Qiao

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  1. D.-X. Gong
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  2. X.-G. Ruan
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  3. J.-F. Qiao
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Correspondence to X.-G. Ruan.

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Gong, DX., Ruan, XG. & Qiao, JF. A neurocomputing model for real coded genetic algorithm with the minimal generation gap. Neural Comput & Applic 13, 221–228 (2004). https://doi.org/10.1007/s00521-004-0407-2

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  • DOI: https://doi.org/10.1007/s00521-004-0407-2

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