UC知道将下面的英文翻译成中文4
来源：百度知道 编辑：UC知道 时间：2024/05/31 06:51:11
For the genetic algorithm, the population encompasses a range of possible outcomes. Solutions are identified purely on a fitness level, and therefore local optima are not distinguished from other equally fit individuals. Those solutions closer to the global optimum will thus have higher fitness values. Successive generations improve the fitness of individuals in the population until the optimisation convergence criterion is met. Due to this probabilistic nature GA tends to the global optimum, however for the same reasons GA models cannot guarantee finding the optimal solution.
The GA consists of four main stages: evaluation, selection, crossover and mutation. The evaluation procedure measures the fitness of each individual solution in the population and assigns it a relative value based on the defining optimisation (or search) criteria. Typically in a non-linear programming scenario, this measure will reflect the objective value of the given model. The selection procedure randomly
The GA consists of four main stages: evaluation, selection, crossover and mutation. The evaluation procedure measures the fitness of each individual solution in the population and assigns it a relative value based on the defining optimisation (or search) criteria. Typically in a non-linear programming scenario, this measure will reflect the objective value of the given model. The selection procedure randomly
对于遗传算法，人口包括一系列可能的结果。解决方案是完全确定的健身水平，因此，当地奥普蒂马没有区别于其他同样适合个人。这些解决方案更接近全局最优解，从而具有较高的健身价值。一代接一代提高健身的个人在人口，直到优化趋同标准得到满足。由于这种概率性遗传倾向的全局最优解，但出于同样的原因遗传模式不能保证找到最优解。
联大包括四个主要阶段：评估，选择，交叉和变异。措施的评价程序的健身每一个人的解决方案中的人口和受让人有相对价值的基础上确定优化（或搜索）的标准。典型的非线性规划的情况下，这一措施将反映客观价值给予模型。选择程序随机选择个人目前的人口发展方面的下一代。各种不同的备选方法（ 2 ）已经提出，但所有后续的想法，优胜劣汰有更大的生存机会。交叉程序有两种选择的个人，并结合他们交叉点，从而创建了两个新的个人。简单（无性）繁殖，也可能会发生复制一个人进入新的人口。程序随机突变的基因修改的个人问题的一个小的突变因素，介绍了进一步的随机性纳入人口。