Self-Programming Artificial Intelligence: Autonomous Learning and Evolutionary Algorithms

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 selfprogramming 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.