How Much Fitness Information Is Enough: Fitness Quantization in Metaheuristic Optimization

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Published: 06.09.2026

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Cinar Ridvan Firat
Batman University
Lale Timur
Batman University

Abstract

We study how many bits of objective-function information population-based metaheuristics require. A decision-level interface separates the true objective from the fitness visible to the optimizer. Differential Evolution (DE), Particle Swarm Optimization (PSO), and a real-coded Genetic Algorithm (GA) are evaluated on five functions in 3,600 paired runs. Under dynamic comparison-relative quantization, Minimum Sufficient Fitness Resolution (MSFR) ranges from 1 to 6 bits, but one bit is worse than full precision for every algorithm-function pair. Fixed-reference controls perform substantially worse, showing that bit depth and range allocation cannot be separated. Low-resolution outcomes also depend on tie policy.

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Data Availability Statement

All data supporting the findings of this study are presented in the text of the scientific work.

Issue

No. 308 (2026): 8th ISPC «Progressive Science and Achievements» (September 6-8, 2026; Dallas, USA).

Section

Radio engineering, Electronics and Electrical engineering
Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Biographies

Cinar Ridvan Firat, Batman University

Assistant Professor in Engineering Faculty, Department of Computer Engineering

Lale Timur, Batman University

Assistant Professor in Engineering Faculty, Department of Electrical and Electronics Engineering

How to Cite

Cinar, R. F., & Lale, T. (2026). How Much Fitness Information Is Enough: Fitness Quantization in Metaheuristic Optimization. Scientific Collection «InterConf», 308, 98–107. https://interconf.openpubarchive.com/index.php/proceeding/article/view/83

References

S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004. doi: 10.1017/CBO9780511804441.

R. Storn and K. Price, “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341 –359, Dec. 1997, doi: 10.1023/A:1008202821328.

R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, no. 1, pp. 33 –57, Oct. 2007, doi: 10.1007/s11721-007-0002-0.

M. Srinivas and L. M. Patnaik, “Genetic algorithms: a survey,” Computer (Long. Beach. Calif)., vol. 27, no. 6, pp. 17–26, Jun. 1994, doi: 10.1109/2.294849. RADIO ENGINEERING, ELECTRONICS AND ELECTRICAL ENGINEERING

P. Liashchynskyi and P. Liashchynskyi, “Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS,” Dec. 2019, Accessed: Sep. 09, 2025. [Online]. Available: http://arxiv.org/abs/1912.06059

R. M. Gray and D. L. Neuhoff, “Quantization,” IEEE Trans. Inf. Theory, vol. 44, no. 6, pp. 2325–2383, 1998, doi: 10.1109/18.720541.

M. Jamil and X. S. Yang, “A literature survey of benchmark functions for global optimisation problems,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no. 2, p. 150-194, 2013, doi: 10.1504/IJMMNO.2013.055204.

F. Wilcoxon, “Individual Comparisons by Ranking Methods,” Biometrics Bulletin, vol. 1, no. 6, p. 80, Dec. 1945, doi: 10.2307/3001968.

S. Holm, “A Simple Sequentially Rejective Multiple Test Procedure,” Scandinavian Journal of Statistics, vol. 6, no. 2, pp. 65 –70, 1979, [Online]. Available: http://www.jstor.org/stable/4615733