| A REAL-VALUED GENETIC ALGORITHM FOR OPTIMIZATION PROBLEM WITH CONTINUOUS VARIABLES |
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| 作者姓名： | 严卫 朱兆达 |
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| 作者单位： | 南京航空航天大学电子工程系 |
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| 基金项目： | 国家自然科学基金,国防科学基金 |
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| 摘 要： | 提出一种用于连续变量函数优化的遗传算法。它由一种简单、适应面广的动态刻度适应值和选择算子、杂交与变异算子，以及这些算子相应的自适应概率组成。该算法经两个常用函数检验，并在图象识别的神经网络权值训练中得到应用。实验结果表明，该算法是一种快速有效的全局优化算法。
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| 关 键 词： | 优化 神经网络 遗传算法 杂交和变异算子 |
A REAL VALUED GENETIC ALGORITHM FOR OPTIMIZATION PROBLEM WITH CONTINUOUS VARIABLES |
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| Yan Wei,Zhu Zhaoda.A REAL-VALUED GENETIC ALGORITHM FOR OPTIMIZATION PROBLEM WITH CONTINUOUS VARIABLES[J].Transactions of Nanjing University of Aeronautics & Astronautics,1997(1). |
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| Authors: | Yan Wei Zhu Zhaoda |
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| Institution: | Yan Wei Zhu Zhaoda Department of Electronic Engineering,NUAA29 Yudao Street,Nanjing 210016,P. R. China |
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| Abstract: | A real valued genetic algorithm(RVGA) for the optimization problem with continuous variables is proposed. It is composed of a simple and general purpose dynamic scaled fitness and selection operator, crossover operator, mutation operators and adaptive probabilities for these operators. The algorithm is tested by two generally used functions and is used in training a neural network for image recognition. Experimental results show that the algorithm is an efficient global optimization algorithm. |
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| Keywords: | optimization neural networks genetic algorithm crossover operator and mutation operator |
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