Exploiting commonality in heuristic search

启发式搜索技术是一种系统的方法，用来为一个最能达到某种目标的解决方案探索一大组可能的解决方案。例如，在婚礼上安排宾客的方法有很多种，但新婚夫妇仍然需要很长时间才能找到他们都能达成一致的一种。对于这个问题，大多数新人会使用迭代改进的形式--从一个解决方案开始，对其进行更改以创建新的解决方案，进行额外的更改，等等，直到找到最后一个可接受的解决方案。还有启发式搜索技术，如遗传算法、模拟退火法、禁忌搜索、ETC都是执行迭代改进的方法。具体地说，这些搜索技术都使用“更改操作符”(创建新解决方案的方法)和“控制策略”(控制哪些新解决方案被接受的方法)。例如，你可能建议交换两个表，而你的伴侣可能会说“不，那更糟”或“是的，但这仍然需要解决”。其他启发式搜索技术往往由他们的控制策略来定义，它们的更改操作符往往大同小异--选择一个候选解决方案并对其进行更改。在理想情况下，被改变的解决方案成分应该是需要改进的东西，而保留的解决方案成分应该是相当好的东西。然而，大多数改变操作员只检查他们改变的东西。他说，最近发展的共性假设(来自遗传算法)表明，对于“好的”解决方案来说，共同的解决方案成分是应该保留的东西。例如，所有将你伴侣的父母放在前面的桌子排列都已经被接受，所以这可能是没有成效的改变。*不幸的是，许多启发式搜索技术(如模拟退火法和禁忌搜索)只适用于单一的解，因此它们的改变算子通常无法访问这些历史信息。因此，探索共性保持在启发式搜索技术中的作用可能会导致关于如何改进它们的新的技术无关的想法。

Heuristic search techniques are systematic methods to explore a large set of possible solutions for the one solution that best achieves some objective.  For example, there are many ways to arrange guests at a wedding reception, but it can still take newlyweds a long time to find one that they can both agree on.  For this problem, most couples would use a form of iterative improvement - start with one solution, make changes to it to create a new solution, make additional changes, etc, until a final acceptable solution is found.   Heuristic search techniques such as genetic algorithms, simulated annealing, tabu search, etc are all methods that perform iterative improvement.  Specifically, these search techniques all employ "change operators" (methods to create new solutions) and "control strategies" (methods to control which new solutions are accepted).  For example, you might suggest swapping two tables, and your partner might say "no, that's worse" or "yes, but this still needs to be addressed".    Heuristic search techniques tend to be defined by their control strategies, and their change operators tend to be more or less the same - take a candidate solution and make changes to it.  Ideally, the solution components that are changed should be things that need improvement, and the solution components that are kept should be the things that are pretty good.  However, most change operators only examine the things that they change.    The recently developed Commonality Hypothesis (from genetic algorithms) suggests that solution components that are common to "good" solutions are the things that should be kept.  For example, all table arrangements that place your partner's parents at the front have been accepted, so this is something that might be unproductive to change.  Unfortunately, many heuristic search techniques (e.g. simulated annealing and tabu search) only work with a single solution, so their change operators do not normally have access to this "historic" information.  Thus, exploring the role of commonality preservation in heuristic search techniques may lead to new technique-independent ideas on how to improve them.