Skip to content

Citation

Citing evobench

If you use evobench in your research, teaching, or industrial applications, we kindly request that you cite the software using the reference provided below. Proper citation ensures appropriate recognition of the work and helps the research community locate and evaluate the methodology used in your studies.

BibTeX

Use the following BibTeX entry for academic citations:

@software{gomez2026evobench,
  title={evobench: Standardized Benchmarking for Evolutionary Algorithms},
  author={G\'omez, Enrique and Galván, Victoria},
  year={2026},
  url={https://github.com/NewtonGomez/evobench},
  note={Software library}
}

APA Style

Gómez, E., & Galván, V. (2026). evobench: Standardized benchmarking for evolutionary algorithms [Software]. Retrieved from https://github.com/NewtonGomez/evobench

Chicago Style

Gómez, Enrique, and Victoria Galván. 2026. "evobench: Standardized Benchmarking for Evolutionary Algorithms." Software. https://github.com/NewtonGomez/evobench

MLA Style

Gómez, Enrique, and Victoria Galván. evobench: Standardized Benchmarking for Evolutionary Algorithms. 2026, github.com/NewtonGomez/evolutionary-benchmarking.

In Research Papers

When describing your experimental methodology, cite evobench as follows:

We used the evobench library (Gómez & Galván, 2026) to implement and benchmark evolutionary algorithms on standardized continuous optimization functions. The library's statistical analysis tools ensured rigorous hypothesis testing through automated decision flow (Shapiro-Wilk normality testing, followed by ANOVA or Kruskal-Wallis testing as appropriate).

In Software Documentation

Include a reference section with the following:

## Dependencies

- evobench 0.1.0+ (Gómez & Galván, 2026)

In README Files

If your project uses evobench, consider adding:

## References

- Gómez, E., & Galván, V. (2026). evobench: Standardized benchmarking for evolutionary algorithms. 
  Retrieved from https://github.com/NewtonGomez/evobench

Accessing the Software

  • Repository: https://github.com/NewtonGomez/evobench
  • Documentation: https://evobench.readthedocs.io/
  • PyPI Package: https://pypi.org/project/evobench/
  • Citation File: See CITATION.cff in the repository root

License

evobench is released under the MIT License. By using this software, you agree to the terms of the license. For details, see the LICENSE file.

Acknowledgments

This work is supported by the research community's commitment to reproducible computational science and standardized evaluation methodologies in evolutionary computation.

Contact

For citation inquiries or contributions, please contact the authors through the GitHub repository: https://github.com/NewtonGomez/evobench

Additional Resources

Key publications in evolutionary algorithm benchmarking:

  • Hansen, N., Auger, A., Finck, S., & Ros, R. (2016). Real-parameter optimization benchmarking 2016: fun use of the COCO/BBOB tools and beyond. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1169–1176).

  • Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2013). Problem definitions and evaluation criteria for the CEC2013 special session and competition on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University and Nanyang Technological University, 1201(2013), 48.

  • López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L. P., Birattari, M., & Stützle, T. (2016). The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3, 43–58.

Statistical Testing References

  • Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.

  • García, S., Molina, D., Lozano, M., & Herrera, F. (2009). A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: A case study with the CEC'05 special session on real parameter optimization. Journal of Heuristics, 15(6), 617–644.