Authors
Fu-Xing Long, Diederick Vermetten, Anna V. Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein
Publication date
2023/11
Conference
Proceedings of the 15th International Joint Conference on Computational Intelligence (ECTA 2023, part of IJCCI)
Pages
119-130
Publisher
SciTePress
Description
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years. Within topics such as instance space analysis (ISA), the generation of new problems can provide new benchmarks which are not yet explored in existing research. Beyond that, this function generation can also be exploited for solving complex real-world optimization problems. By generating functions with similar properties to the target problem, we can create a robust test set for algorithm selection and configuration. However, the generation of functions with specific target properties remains challenging. While features exist to capture low-level landscape properties, they might not always capture the intended high-level features. We show that a genetic programming (GP) approach guided by these exploratory landscape analysis (ELA) properties is not always able to find satisfying functions. Our results suggest that careful considerations of the weighting of landscape properties, as well as the distance measure used, might be required to evolve functions that are sufficiently representative to the target landscape.
Total citations
20246
Scholar articles
FX Long, D Vermetten, AV Kononova, R Kalkreuth… - arXiv preprint arXiv:2305.15245, 2023