UC知道将下面的英文翻译出中文
来源：百度知道 编辑：UC知道 时间：2024/07/04 09:45:02
Genetic algorithms (GA) are an evolutionary optimisation approach which are an alternative to traditional optimisation methods. GA are most appropriate for complex non-linear models where location of the global optimum is a difficult task. It may be possible to use GA techniques to consider problems which may not be modelled as accurately using other approaches. Therefore, GA appears to be a potentially useful approach.
GA follows the concept of solution evolution by stochastically developing generations of solution populations using a given fitness statistic (for example, the objective function in mathematical programmes). They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Due to the probabilistic development of the solution, GA do not guarantee optimality even when it may be reached. However, they are likely to be close to the global optimum. This proba
GA follows the concept of solution evolution by stochastically developing generations of solution populations using a given fitness statistic (for example, the objective function in mathematical programmes). They are particularly applicable to problems which are large, non-linear and possibly discrete in nature, features that traditionally add to the degree of complexity of solution. Due to the probabilistic development of the solution, GA do not guarantee optimality even when it may be reached. However, they are likely to be close to the global optimum. This proba
遗传算法(GA)是一个进化的最优化方法,替代传统的优化方法。GA最适合复杂的非线性模型在全局最优位置是一项艰巨的任务。它可以用遗传技术来考虑问题,不得为准确地使用其他方法。因此,似乎是一种潜在的有用的方法。
遗传算法的进化的概念发展解决方案的后代数量指标随机给定数据使用健身(例如,目标函数在数学课程)。他们特别适用于大、非线性问题的性质,并可能离散特征,传统上增添了程度的复杂性的解决方案。由于概率发展的遗传算法中不保证最优,甚至当它可能达成。然而,他们很可能会接近全局最优解。这个概率本质的溶液的原因并不包含由局部最优解。
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遗传算法（ GA ）是一个渐进的优化方法是替代传统的优化方法。遗传算法是最适合于复杂的非线性模型的位置在全球最佳的是一项艰巨的任务。有可能利用遗传技术考虑的问题，可能无法仿效准确使用其他办法。因此，大会似乎是一个可能有用的办法。
算法如下解决方案的概念演化的随机发展中国家几代解决人口使用特定健身统计（例如，目标函数的数学方案） 。它们尤其适用于大的问题，这些问题，非线性和可能离散的性质，功能，增加了传统的复杂程度的解决办法。由于概率发展的解决方案，遗传算法并不能保证最优，即使它可能达成。然而，他们可能会接近全局最优。这种概率性的解决办法，也是他们的理由是不包含当地奥普蒂马。