• 遗传算法主要特点在于简单通用鲁棒性强

    The main characteristic of the GA is that it is simple, universal and robust.

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  • 遗传算法一种模拟自然进化而提出简单高效优化组合方法

    Genetic algorithms is an efficiently combined and optimized method by simulating the nature evolution.

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  • 最后一个简单实例说明使用遗传算法生成基本数据类型测试数据过程

    At last we use a simple example to show the process of test data of basic type generation with GA.

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  • 针对简单遗传算法局限性提出基于粒子算法遗传算法

    According to the limitations of simple genetic algorithms, design genetic algorithms based on particle swarm.

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  • 遗传算法优化BP神经网络在收敛速度泛化能力简单BP神经网络要好,模拟结果接近于真实值。

    The constringency speed and generalization ability of optimized BPNN model are better than that of simple BPNN model, and the simulation result is close to reality.

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  • 算法简单遗传算法编码方式选择策略交叉变异操作进行了改进使搜索效率有了很大提高,有效地避免了早期收敛

    This algorithm improves on encoding, selection, crossover and mutation operations of SGA. It enhances searching efficiency greatly, and avoids effectively premature convergence.

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  • 本文首先介绍了遗传算法解决简单约束车辆路径问题应用改进了交叉算子,为研究有时间窗装卸问题遗传算法作了充分准备

    In this paper, Genetic Algorithms (GA) for VRP with simple conditions was introduced and cross operator in GA was improved. These researches made full preparation for us to study PDPTW.

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  • 实验结果表明自适应遗传算法用于码本设计具有运算简单聚类能力等优点,有着广泛应用前景。

    Thus improving the performance of the algorithms. Experimental results show that applying AGA to image VQ codebook design is computationally simpler and has a strong clustering abl...

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  • 同时,通过实验证明了系统简单遗传算法生成测试数据优越性

    And by the experiment that is designed in this paper, the advantage of the system is proved.

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  • 优化BP神经网络模型简单BP神经网络进行比较实验结果表明,基于遗传算法优化BP神经网络模型在耕地分等评价工作应用完全可行

    After the comparison of optimized BPNN model and simple BPNN model, the result shows that, it is completed feasible to use optimized BPNN model in cultivated land classification work.

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  • 方法原来随机选择过程简单减少遗传算法种群规模

    This method is more simple than original stochastic choice process , also reduces the GA population scale.

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  • 方法原来随机选择过程简单减少遗传算法种群规模

    This method is more simple than original stochastic choice process , also reduces the GA population scale.

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