Adaptive Multi-Layer Random Generator: Toward Self-Regulating Pseudorandomness

Bazghandi, Ali

doi:10.22061/jecei.2026.12518.887

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	Adaptive Multi-Layer Random Generator: Toward Self-Regulating Pseudorandomness
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 6، دوره 14، شماره 2، مهر 2026، صفحه 365-376 اصل مقاله (1.35 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2026.12518.887
نویسنده
Ali Bazghandi^*
Faculty of Computer and IT Engineering, Shahrood University of Technology, Shahrood, Iran.
تاریخ دریافت: 04 آبان 1404، تاریخ بازنگری: 30 بهمن 1404، تاریخ پذیرش: 04 اسفند 1404
چکیده
Background and Objectives: Random number generation is essential in simulation, cryptography, and statistical modeling. Classical PRNGs such as the Linear Congruential Generator and Mersenne Twister are efficient but exhibit predictability and correlation. Newer families like PCG and BRG improve statistical balance yet remain static after initialization, while chaotic and neural methods face reproducibility and stability issues. To overcome these limits, we propose the Adaptive Multi-Layer Random Generator (AMLRG), designed to deliver self-regulating pseudorandomness through adaptive feedback and hybrid entropy sources. Methods: AMLRG combines three layers: (i) an index generator based on linear, logistic, or chaotic processes, (ii) a uniform distribution module, and (iii) an adaptive feedback system that tunes parameters in real time. Online diagnostics—Kolmogorov–Smirnov tests, autocorrelation analysis, and Shannon entropy—direct dynamic adjustment. Implemented in Python, the system produces binary streams and diagnostic plots. Evaluation involved ablation studies (removing feedback, switching, or stratification), comparison with LCG, PCG, BRG, and logistic-only baselines, and validation using Dieharder, TestU01, and NIST SP 800-22. Results: AMLRG produced lower KS distances, near-zero autocorrelation, and entropy close to theoretical maxima, outperforming all baselines. Ablation confirmed the contribution of each layer to statistical quality. Results show stable behavior across 200,000 values, with speed comparable to PCG but greater adaptability. Conclusion: AMLRG introduces dynamic correction that improves independence and uniformity in pseudorandom sequences. Its layered architecture suits engineering simulations, adaptive systems, and security-sensitive statistical preprocessing. Future work will target non-uniform distributions, GPU acceleration, and hardware implementation.
کلیدواژه‌ها
Entropy Correction؛ Statistical Testing؛ Kolmogorov–Smirnov Analysis؛ Autocorrelation Metrics؛ Throughput Optimization

مراجع
[1] D. E. Knuth, The Art of Computer Programming, vol. 2: Seminumerical Algorithms, 2nd ed. Reading, MA: Addison-Wesley, 1981. [2] M. Matsumoto, T. Nishimura, “Mersenne Twister: A 623-dimensionally equidistributed uniform pseudo-random number generator,” ACM Trans. Model. Comput. Simul., 8(1): 3–30, 1998. [3] J. E. Gentle, Random Number Generation and Monte Carlo Methods, 2nd ed. New York: Springer, 2003. [4] S. K. Park, K. W. Miller, “Random number generators: Good ones are hard to find,” Commun. ACM, 31(10): 1192–1201, 1988. [5] P. L’Ecuyer, “Random numbers for simulation,” Commun. ACM, 33(10): 85–97, 1990. [6] A. Inan, “Statistical analysis of prime number generators putting encryption at risk,” in Advances in Security, Networks, and Internet of Things: 3-16, 2021. [7] P. L’Ecuyer, “Good parameters and implementations for combined multiple recursive random number generators,” Oper. Res., 47(1): 159–164, 1999. [8] M. E. O’Neill, “PCG: A family of simple fast space-efficient statistically good algorithms for random number generation,” ACM Trans. Math. Softw., 45(3): 1–35, 2019. [9] M. Tantaoui et al., “Impact of pseudo-random number generators on dosimetric parameters in validation of medical linear accelerator head simulation for 6 MV photons using the GATE/GEANT4 platform,” Quantum Beam Sci., 9(2): 16, 2025. [10] B. Antunes, “Statistical quality and reproducibility of pseudo-random number generators in machine learning technologies,” Int. J. Data Informatics Intell. Comput., 4(3): 23-32, 2025. [11] A. Bazghandi, “A bipartite approach for generating uniform random numbers,” NPECE01_271, 2014. [12] L. Palacios-Luengas et al., “Enhanced chaotic pseudorandom number generation using multiple Bernoulli maps with field programmable gate array optimizations,” Information, 15(11): 667, 2024. [13] M. Garcia-Bosque, A. Pérez-Resa, C. Sánchez-Azqueta, C. Aldea, S. Celma, “Chaos-based bitwise dynamical pseudorandom number generator on FPGA,” arXiv:2401.15035, 2024. [14] P. S. Paul, M. Sadia, M. S. Hasan, “Design of a dynamic parameter-controlled chaotic-PRNG in a 65 nm CMOS process,” arXiv:2101.01173, 2021. [15] L. Pasqualini, M. Parton, “Pseudo-random number generation through reinforcement learning and recurrent neural networks,” arXiv:2011.02909, 2020. [16] M. L. R. Becerra et al., “Design and hardware implementation of a highly flexible PRNG system for NIST-validated pseudorandom sequences,” Chips, 4(2), 23, 2025. [17] M. D. Gupta, R. K. Chauhan, “Hardware efficient pseudo-random number generator using Chen chaotic system on FPGA,” J. Circuits Syst. Comput., 31(3), 2021. [18] F. Yu et al., “Design and FPGA implementation of a pseudo-random number generator based on a Hopfield neural network under electromagnetic radiation,” Front. Phys., 9, 2021. [19] R. G. Brown, Dieharder: A Random Number Test Suite, 2004. [20] J. von Neumann, “Various techniques used in connection with random digits,” Appl. Math. Ser., 12: 36–38, 1949. [21] T. E. Hull, A. R. Dobell, “Random number generators,” SIAM Rev., 4(3): 230–254, 1962. [22] D. Cirauqui et al., “Comparing pseudo- and quantum-random number generators with Monte Carlo simulations,” APL Quantum, 1(3), 2024. [23] V. Patidar et al., “A novel approach to pseudorandom number generation using Hamiltonian conservative chaotic systems,” Front. Phys., 12, 2025. [24] C. Fei et al., “EPRNG: Effective pseudo-random number generator on the Internet of Vehicles using deep convolution generative adversarial network,” Sensors, 16(1): 21, 2024. [25] K. Okada et al., “Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers,” PLOS ONE, 18(6): e0287025, 2023. [26] S. Boanca, “Pseudorandom number generators, perfect learning and model visualization with neural networks: Expanding on LFSRs and Geffe,” in Proc. 10th International Conference on Internet of Things, Big Data and Security IoTBDS (IoTBDS), 2025. [27] X. Wu et al., “Pseudorandom number generators based on neural network models: RNN, GAN and RL-based designs,” Front. Comput. Sci., 37, 2025. [28] M. A. Bouke, O. I. Alramli, A. Abdullah, M. H. Zurina, “Entropy mixing networks: Enhancing pseudo-random number generators with lightweight dynamic entropy,” Int. J. Data Informatics Intell. Comput., 9(1), 2025. [29] B Bizu et al., “Analysis of mixing of pseudo-random number generators with statistical tests,” in Proc. 2025 5th International Conference on Expert Clouds and Applications (ICOECA), Mar., 2025. [30] P. L’Ecuyer, R. Simard, “TestU01: A C library for empirical testing of random number generators,” ACM Trans. Math. Softw., 33(4): 22:1–22:40, 2007.
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Adaptive Multi-Layer Random Generator: Toward Self-Regulating Pseudorandomness