# Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review

arXiv: https://arxiv.org/abs/1806.04563 · HTML: https://mlsanity.com/paper/1806.04563/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions](https://mlsanity.com/paper/2202.02163/) (2022)
- [Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems](https://mlsanity.com/paper/2501.11924/) (2025)
- [The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways](https://mlsanity.com/paper/2501.07515/) (2025)
- [The Evolutionary Computation Methods No One Should Use](https://mlsanity.com/paper/2301.01984/) (2023)
- [Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems](https://mlsanity.com/paper/2402.16455/) (2024)
- [Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems](https://mlsanity.com/paper/2212.06875/) (2022)
- [An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models](https://mlsanity.com/paper/2601.12723/) (2026)

## Abstract (arXiv)

Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presents common principles that need to be taken into account when considering benchmarking problems for constrained optimization. Current benchmark environments for testing Evolutionary Algorithms are reviewed in the light of these principles. Along with this line, the reader is provided with an overview of the available problem domains in the field of constrained benchmarking. Hence, the review supports algorithms developers with information about the merits and demerits of the available frameworks.