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A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms

机译：进化算法的象棋评级系统：一种用于进化算法比较和排名的新方法

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摘要

The Null Hypothesis Significance Testing (NHST) is of utmost importance for comparing evolutionary algorithms as the performance of one algorithm over another can be scientifically proven. However, NHST is often misused, improperly applied and misinterpreted. In order to avoid the pitfalls of NHST usage this paper proposes a new method, a Chess Rating System for Evolutionary Algorithms (CRS4EAs) for the comparison and ranking of evolutionary algorithms. A computational experiment in CRS4EAs is conducted in the form of a tournament where the evolutionary algorithms are treated as chess players and a comparison between the solutions of two algorithms on the objective function is treated as one game outcome. The rating system used in CRS4EAs was inspired by the Glicko-2 rating system, based on the Bradley-Terry model for dynamic pairwise comparisons, where each algorithm is represented by rating, rating deviation, a rating/confidence interval, and rating volatility. The CRS4EAs was empirically compared to NHST within a computational experiment conducted on 16 evolutionary algorithms and a benchmark suite of 20 numerical minimisation problems. The analysis of the results shows that the CRS4EAs is comparable with NHST but may also have many additional benefits. The computations in CRS4EAs are less complicated and sensitive than those in statistical significance tests, the method is less sensitive to outliers, reliable ratings can be obtained over a small number of runs, and the conservativity/liberality of CRS4EAs is easier to control.
机译：零假设显着性测试（NHST）对于比较进化算法至关重要，因为可以科学证明一种算法相对于另一种算法的性能。但是，NHST经常被滥用，使用不当和被误解。为了避免使用NHST的麻烦，本文提出了一种新方法，即进化算法国际象棋评级系统（CRS4EAs），用于对进化算法进行比较和排名。 CRS4EA中的计算实验是以锦标赛的形式进行的，其中进化算法被视为国际象棋棋手，而两种算法在目标函数上的解决方案之间的比较被视为一种游戏结果。 CRS4EA中使用的评级系统是受Glicko-2评级系统启发的，该系统基于Bradley-Terry模型进行动态成对比较，其中每种算法均由评级，评级偏差，评级/置信区间和评级波动性表示。在对16种进化算法和20个数值最小化问题的基准套件进行的计算实验中，将CRS4EAs与NHST进行了经验比较。结果分析表明，CRS4EA与NHST相当，但也可能具有许多其他好处。 CRS4EAs中的计算比统计显着性测试中的计算复杂度和敏感性低，该方法对异常值的敏感性较低，可以在少量运行中获得可靠的评级，并且CRS4EAs的保守性/自由度更易于控制。

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