评估噪声优化算法：首次命中时间是有问题的
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
摘要：
任何进化算法的一个关键部分是适应度评估。当适应度评估被噪声破坏时，就像在许多现实问题中由于各种类型的不确定性而发生的那样，需要一种策略来应对这种情况。Rescue是最常见的策略之一，其中每个解决方案都被评估多次，以减少适应度估计的方差。在评估噪声优化算法的性能时，一个关键的考虑因素是算法的停止条件。运行时分析中经常使用的停止条件，称为“首次命中时间”，是一旦遇到最优解就停止算法。然而，这是不现实的现实世界中的问题，如果最佳的解决方案是已知的，就没有必要搜索it.This本文认为，使用的首次命中时间，尽管是一种常用的方法，是显着的缺陷和高估了许多算法的质量在现实世界中的情况下，最佳的是不知道提前，并真正搜索。一个更好的替代方案是测量算法在固定评估预算之后返回的解决方案的质量，即，专注于最终解决方案的质量。本文认为，专注于最终的解决方案的质量是更现实的，并演示的情况下，每个算法的评估方法产生的结果导致非常不同的结论，每个嘈杂的优化算法的质量。
英文摘要：
A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various types of uncertainty, a strategy is needed in order to cope with this. Resampling is one of the most common strategies, whereby each solution is evaluated many times in order to reduce the variance of the fitness estimates. When evaluating the performance of a noisy optimisation algorithm, a key consideration is the stopping condition for the algorithm. A frequently used stopping condition in runtime analysis, known as "First Hitting Time", is to stop the algorithm as soon as it encounters the optimal solution. However, this is unrealistic for real-world problems, as if the optimal solution were already known, there would be no need to search for it. This paper argues that the use of First Hitting Time, despite being a commonly used approach, is significantly flawed and overestimates the quality of many algorithms in real-world cases, where the optimum is not known in advance and has to be genuinely searched for. A better alternative is to measure the quality of the solution an algorithm returns after a fixed evaluation budget, i.e., to focus on final solution quality. This paper argues that focussing on final solution quality is more realistic and demonstrates cases where the results produced by each algorithm evaluation method lead to very different conclusions regarding the quality of each noisy optimisation algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
