genetic-algorithm - 有没有办法借助遗传算法检测公式？

Question

我试图找出两个图像（比如说“image1”和“image2”）如何相互匹配。为“image2”相对于“image1”的每个可能位置计算了几个参数。我有经验公式，根据这些参数为每个位置给出“分数”。

我试图在神经网络的帮助下匹配图像部分，但失败了：经验公式效果更好。从这里我开始考虑借助遗传算法改进这个公式。

所以，问题是：我有一堆图像对，对于每一对我都知道“正确”的匹配位置。遗传算法可以用在这样的事情上吗？有什么例子吗？

建议和链接表示赞赏。

谢谢。

Answer 1

基本上，是的！您的评分函数的参数可能是您的 GA 将要发展的参数。如果您的参数在真实域中，您可能希望使用真实编码遗传算法或进化策略 (CMA-ES)。

交叉存在几种可能的选择：

• 平均/中级
• 混合阿尔法 (BLX-a)
• 混合-Alpha-Beta (BLX-ab)
• 离散的
• 启发式
• 当地的
• 随机凸
• 模拟二进制 (SBX)
• 单点

还有一些变异算子：

• 正态分布 N(0, sigma) -> 例如随着时间的推移适应减少 sigma
• 均匀分布（在某些位置）
• 多项式变异

另一种适用于实际编码问题的元启发式算法是粒子群优化 (PSO)。

With genetic programming you're going to evolve a formula (e.g. a tree). I'm not so sure why you mention it, maybe I still misunderstand something. Clarify your problem, just in case.

EDIT:

Okay it seems it's not the weights that you want to optimize, but the whole formula. Still, genetic algorithms can be used for this representation as well. I want to mention HeuristicLab due to its good support for genetic programming.

I assume you have a more complex problem since you want to optimize the scoring function, and still have another algorithm for optimizing the placement according to that scoring function. You could try an easy approach and generate a dataset with several positions predefined and the features calculated accordingly. Then you could formulate a classification problem and find a model that allows you to identify those positionings that are optimal.