When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of problems. In this paper, we investigate several of these problem transformations and show how they influence the low-level landscape features of a set of 5 problems from the CEC2022 benchmark suite. Our results highlight that even relatively small transformations can significantly alter the measured landscape features. This poses a wider question of what properties we want to preserve when creating problem transformations, and how to fairly measure them.

翻译：在对优化启发式算法进行基准测试时，需避免算法利用问题构造中的偏差。一种可行方法是为每个问题提供不同版本，通过施加变换确保算法具备应对各类问题的机制。本文研究了若干问题变换方法，并揭示其如何影响CEC2022基准测试套件中5个问题的低层景观特征。研究结果表明，即便微小变换也会显著改变测得的景观特征。这引发了一个更广泛的问题：在构建问题变换时需保留哪些特性，以及如何对其进行公平度量。