Challenges of ELA-guided Function Evolution using Genetic Programming

arXiv (Cornell University)

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 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.

Authors 7

1. Fu Xing Long
2. Diederick Vermetten Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , Netherlands

3. Anna V. Kononova Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , Netherlands

4. Roman Kalkreuth Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , Netherlands

5. Kaifeng Yang

   University of Applied Sciences Upper Austria

   Affiliation as printed

   University of Applied Sciences Upper Austria Hagenberg , Austria

6. Thomas Bäck
7. Niki van Stein Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   LIACS , Leiden University Leiden , Netherlands