Impact of Spatial Transformations on Exploratory and Deep-Learning Based Landscape Features of CEC2022 Benchmark Suite

Yin, H.; Vermetten, D.L.; Ye, F.; Bäck, T.H.W.; Kononova, A.V. (2024)

Article in monograph or in proceedings

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 problems from the Congress on Evolutionary Computation 2022 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 measure them fairly.

• All authors: Yin, H.; Vermetten, D.L.; Ye, F.; Bäck, T.H.W.; Kononova, A.V.
• Editor(s): Macelloni, F.; Madani, K.; Stein, N. van; Filipe, J.
• Date: 2024
• Title of host publication: Proceedings of the 16th International Joint Conference on Computational Intelligence ECTA
• Pages: 60 - 71
• ISBN (print): 978-989-758-721-4
• DOI: doi:10.5220/0012933900003837
• Link: https://www.scitepress.org/Link.aspx?doi=10.5220/0012933900003837
• Name: 1