遗传/进化算法和局部最小值/最大值
我读过一些帖子和文章，建议使用模拟退火等方法来避免局部最小值/最大值问题。
我不明白如果你一开始就有足够大的随机群体，为什么这是必要的。
这是否只是另一项检查，以确保初始群体实际上足够大且随机？或者这些技术只是产生“良好”初始种群的替代方法？
I have run across several posts and articles that suggests using things like simulated annealing to avoid the local minima/maxima problem.
I don't understand why this would be necessary if you started out with a sufficiently large random population.
Is it just another check to insure that the initial population was, in fact, sufficiently large and random? Or are those techniques just an alternative to producing a "good" initial population?
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模拟退火是一种概率优化技术——它不应该为您提供更精确的答案，而应该为您提供更快的近似值。
Simulated annealing is a probabilistic optimization technique -- it's not supposed to give you more precise answers, it's supposed to give you approximations faster.
模拟退火是一种概率技术，其中陷入局部最小值/最大值的机会取决于温度的安排。对于不同类型的问题，调度温度是不同的。进化算法更加稳健，并且不太可能陷入局部最小值/最大值。 SA是概率性的。另一方面，EA 使用变异，在搜索空间中引入随机游走，这就是 EA 获得全局最优的概率更高的原因。
Simulated annealing is probabilistic technique where chance of getting trapped in local minima/maxima depends on scheduling of temperature. Scheduling temperature is different for different types of problems. Evolutionary Algorithm is much more robust and less likely to get trapped in local minima/maxima. SA is probabilistic. On the other hand, EA uses mutation which introduces random walk in search space, that's why EA has higher probability of getting global optima.
首先，模拟退火是最后的手段。有更好、更高效、更有效的方法来发现局部最小值的位置。
更好的检查是使用统计方法来发现有关数据集的信息，例如方差或标准差。
First of all, simulated annealing is a last resort method. There are far better, more efficient, and more effective methods of discovering where the local minima are found.
A better check would be to use a statistical method to uncover information about your data set such as variance or standard deviation.