Abstract
This paper combines genetic algorithms and neural networks to recognize vehicle license plate characters. We train the neural networks using a genetic algorithm to find optimal weights and thresholds. The traditional genetic algorithm is improved by using a real number encoding method to enhance the networks weight and threshold accuracy. At the same time, we use a variety of crossover operations in parallel, which broadens the range of the species and helps the search for the global optimal solution. An adaptive mutation rate both ensures the diversity of the species and makes the algorithm convergence more rapidly to the global optimum. Experiments show that this method greatly improves learning efficiency and convergence speed.
This work was supported by National Science Foundation of China under Grant 61273308.
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Xiaobin, W., Hao, L., Lijuan, W., Qu, H. (2013). Genetic Algorithm Based Neural Network for License Plate Recognition. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_48
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DOI: https://doi.org/10.1007/978-3-642-39065-4_48
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