Challenges of ELA-Guided Function Evolution Using Genetic Programming

Fu Xing Long, Diederick Vermetten, Anna Kononova, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki van Stein

2023

Abstract

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 expensive real-world optimization problems. By generating fast-to-evaluate functions with similar optimization properties to the target problems, we can create a test set for algorithm selection and configuration purposes. 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 it is challenging to find satisfying functions through a genetic programming (GP) approach guided by the exploratory landscape analysis (ELA) properties. Our results suggest that careful considerations of the weighting of ELA properties, as well as the distance measure used, might be required to evolve functions that are sufficiently representative to the target landscape.

Paper Citation

in Harvard Style

Long F., Vermetten D., Kononova A., Kalkreuth R., Yang K., Bäck T. and van Stein N. (2023). Challenges of ELA-Guided Function Evolution Using Genetic Programming. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-674-3, SciTePress, pages 119-130. DOI: 10.5220/0012206200003595

in Bibtex Style

@conference{ecta23,
author={Fu Xing Long and Diederick Vermetten and Anna Kononova and Roman Kalkreuth and Kaifeng Yang and Thomas Bäck and Niki van Stein},
title={Challenges of ELA-Guided Function Evolution Using Genetic Programming},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2023},
pages={119-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012206200003595},
isbn={978-989-758-674-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Challenges of ELA-Guided Function Evolution Using Genetic Programming
SN - 978-989-758-674-3
AU - Long F.
AU - Vermetten D.
AU - Kononova A.
AU - Kalkreuth R.
AU - Yang K.
AU - Bäck T.
AU - van Stein N.
PY - 2023
SP - 119
EP - 130
DO - 10.5220/0012206200003595
PB - SciTePress