IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 5 2025
DOI: 10.56201/ijcsmt.vol.11.no5.2025.pg77.92

---

Self-Programming Artificial Intelligence: Autonomous Learning and Evolutionary Algorithms

Neenaalebari Henry James, Donald S Ene, Godwin Fred Lenu

---

Abstract

This paper presents a hybrid framework for self-programming artificial intelligence (AI), integrating reinforcement learning (RL), genetic programming (GP), neural architecture search (NAS), and meta-learning to enable autonomous code evolution with minimal human intervention. The proposed system is designed to optimize performance, adaptability, and scalability across a range of complex tasks. Through empirical evaluations in simulated environments such as CartPole-v1 and MountainCarContinuous-v0, the hybrid model demonstrated superior task completion rates, faster adaptation, and reduced code complexity compared to baseline models. Key implementation strategies include Proximal Policy Optimization for RL, evolutionary optimization via tournament selection and Pareto-front analysis in GP, and architecture tuning through NAS. The framework’s continuous learning and feedback loops enable real-time optimization and deployment. Ethical considerations, such as transparency, safety, and regulatory compliance, are also addressed to ensure responsible AI development. This research highlights the transformative potential of self- programming AI across diverse sectors, including healthcare, finance, cybersecurity, and education, while acknowledging challenges in computational efficiency, interpretability, and ethical alignment. The findings affirm the viability of self-evolving AI systems as a foundational advancement in autonomous, intelligent technologies.

keywords:

Self-Programming Artificial Intelligence, Autonomous Learning, Evolutionary

---

References:

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L.
(2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC
Conference on Computer and Communications Security, 308–318.
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mane, D. (2016). Concrete
problems in AI safety. arXiv preprint arXiv:1606.06565.
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection
using machine learning techniques: A comparative analysis. Journal of Applied Security
Research, 12(1), 1-15.
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish &
W. M. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316-
334). Cambridge University Press.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital
future. W. W. Norton & Company.
Chakrabarti, S., Pan, A., & Chakrabarti, P. (2021). Role of artificial intelligence in drug
discovery. Drug Discovery Today, 26(1), 80–89.
Chen, T., & Lee, C. (2020). Deep reinforcement learning in algorithmic trading. Quantitative
Finance, 20(5), 753–767.
De Jong, K. A. (2006). Evolutionary computation: A unified approach. MIT Press.
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q. V., ... & Ng, A. Y. (2012).
Large-scale distributed deep networks. Advances in Neural Information Processing
Systems, 25, 1223–1231.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine
learning. arXiv preprint arXiv:1702.08608.
Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural architecture search: A survey. Journal
of Machine Learning Research, 20(55), 1–21.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017).
Dermatologist-level classification of skin cancer with deep neural networks. Nature,
542(7639), 115–118.
Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of
deep networks. Proceedings of the 34th International Conference on Machine Learning,
1126–1135.
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—
Explainable artificial intelligence. Science Robotics, 4(37).
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan
Press.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and
implications for teaching and learning. Center for Curriculum Redesign.
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of
natural selection. MIT Press.
Liu, H., Simonyan, K., & Yang, Y. (2019). DARTS: Differentiable architecture search.
International Conference on Learning Representations (ICLR).
Liu, Z., Sun, M., Zhou, T., Huang, G., & Darrell, T. (2018). Rethinking the value of network
pruning. Proceedings of the 7th International Conference on Learning Representations.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An
argument for AI in education. Pearson Education.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... &
Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature,
518(7540), 529–533.
Nguyen, T. T., Nguyen, D. C., Huh, E. N., & Pathirana, P. N. (2019). Genetic reinforcement
learning for task offloading in mobile edge computing. IEEE Access, 7, 11779–11790.
Nichol, A., Achiam, J., & Schulman, J. (2018). On first-order meta-learning algorithms. arXiv
preprint arXiv:1803.02999.
Orike, S., & Ene, D. S. (2023). Meta-Learning: Unleashing the Power of Self-Improving
Artificial Intelligent (AI) Systems. Journal of Advances in Computational Intelligence
Theory, 5(3).
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... &
Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477–486.
Salustowicz, R. P., & Schmidhuber, J. (1997). Probabilistic incremental program evolution.
Evolutionary Computation, 5(2), 123–141.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy
optimization algorithms. arXiv preprint arXiv:1707.06347.
Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2016). Safe, multi-agent, reinforcement
learning for autonomous driving. arXiv preprint arXiv:1610.03295.
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks
against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP),
3–18.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT
Press.
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural
networks. arXiv preprint arXiv:1905.11946.
Yang, G., Li, H., & Wang, Z. (2020). Robotics and artificial intelligence in healthcare: A new
era of precision medicine. Journal of Medical Internet Research, 22(10), e20938.
Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
4207–4215.

DOWNLOAD PDF

---

Back