遗传算法是一种模拟自然进化而提出的简单高效的优化组合方法。
Genetic algorithms is an efficiently combined and optimized method by simulating the nature evolution.
针对简单遗传算法的局限性，提出了基于粒子群算法的遗传算法。
According to the limitations of simple genetic algorithms, design genetic algorithms based on particle swarm.
遗传算法优化的BP神经网络在收敛速度和泛化能力上都较简单的BP神经网络要好，模拟结果更接近于真实值。
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.
该算法对简单遗传算法的编码方式、选择策略、交叉和变异操作进行了改进，使搜索效率有了很大的提高，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.
本文首先介绍了遗传算法在解决简单约束车辆路径问题上的应用，改进了交叉算子，为研究有时间窗装卸问题的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.
实验结果表明，将自适应遗传算法用于码本设计，具有运算简单、聚类能力强等优点，有着广泛的应用前景。
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...
同时，通过实验证明了该系统的较简单遗传算法生成测试数据的优越性。
And by the experiment that is designed in this paper, the advantage of the system is proved.
将优化后的BP神经网络模型和简单的BP神经网络进行比较，实验结果表明，基于遗传算法优化的BP神经网络模型在耕地分等评价工作中的应用完全可行。
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.
该方法较原来的随机选择过程简单，且减少了遗传算法的种群规模。
This method is more simple than original stochastic choice process , also reduces the GA population scale.
该方法较原来的随机选择过程简单，且减少了遗传算法的种群规模。
This method is more simple than original stochastic choice process , also reduces the GA population scale.
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