论文标题
基于试验的优势使非参数测试可以比较随机优化器的速度和准确性
Trial-Based Dominance Enables Non-Parametric Tests to Compare both the Speed and Accuracy of Stochastic Optimizers
论文作者
论文摘要
当基准测试结果是有序的，例如多个试验的最终适应性值时，非参数测试可以确定两种随机优化算法的更好。但是，对于许多基准，一旦达到预先指定的目标值，试验也可以终止。当只有一些试验达到目标值时，两个变量表征了试验结果：达到目标值（是否）及其最终健身价值所需的时间。本文介绍了一种在此两变量试验数据集上强加线性秩序的简单方法，以便传统的非参数方法可以确定当两者都没有主导时可以确定更好的算法。我们用Mann-Whitney U检验说明了方法。一个模拟表明，在识别两种算法的更好的任务时，U评分比优势更有效。我们通过让他们确定CEC 2022特殊会议的获奖者和实时参数数值优化的竞争来测试U得分。
Non-parametric tests can determine the better of two stochastic optimization algorithms when benchmarking results are ordinal, like the final fitness values of multiple trials. For many benchmarks, however, a trial can also terminate once it reaches a pre-specified target value. When only some trials reach the target value, two variables characterize a trial's outcome: the time it takes to reach the target value (or not) and its final fitness value. This paper describes a simple way to impose linear order on this two-variable trial data set so that traditional non-parametric methods can determine the better algorithm when neither dominates. We illustrate the method with the Mann-Whitney U-test. A simulation demonstrates that U-scores are much more effective than dominance when tasked with identifying the better of two algorithms. We test U-scores by having them determine the winners of the CEC 2022 Special Session and Competition on Real-Parameter Numerical Optimization.
