Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2018.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2; 2026: 1.
7 papers.
- 2022COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutionscited by 10
- 2025Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systemscited by 4
- 2025The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathwayscited by 3
- 2023The Evolutionary Computation Methods No One Should Usecited by 2
- 2024Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problemscited by 1
- 2022Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problemscited by 0
- 2026An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Modelscited by 0
- 2026An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models19 Jan 2026
- 2025Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems21 Jan 2025
- 2025The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways13 Jan 2025
- 2024Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems26 Feb 2024
- 2023The Evolutionary Computation Methods No One Should Use5 Jan 2023
- 2022Are metaheuristics worth it? A computational comparison between nature-inspired and deterministic techniques on black-box optimization problems13 Dec 2022
- 2022COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions4 Feb 2022