# Evolving Benchmark Functions to Compare Evolutionary Algorithms via Genetic Programming

arXiv: https://arxiv.org/abs/2403.14146 · HTML: https://mlsanity.com/paper/2403.14146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [Evolving test instances of the Hamiltonian completion problem](https://mlsanity.com/paper/2011.02291/) (2020)
- [Challenges of ELA-guided Function Evolution using Genetic Programming](https://mlsanity.com/paper/2305.15245/) (2023)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Evaluation Function: A Multi-Metric Approach for Optimizing Demand Forecasting Models](https://mlsanity.com/paper/2508.13057/) (2025)
- [An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models](https://mlsanity.com/paper/2601.12723/) (2026)

## Abstract (arXiv)

In this study, we use Genetic Programming (GP) to compose new optimization benchmark functions. Optimization benchmarks have the important role of showing the differences between evolutionary algorithms, making it possible for further analysis and comparisons. We show that the benchmarks generated by GP are able to differentiate algorithms better than human-made benchmark functions. The fitness measure of the GP is the Wasserstein distance of the solutions found by a pair of optimizers. Additionally, we use MAP-Elites to both enhance the search power of the GP and also illustrate how the difference between optimizers changes by various landscape features. Our approach provides a novel way to automate the design of benchmark functions and to compare evolutionary algorithms.