Leveraging Reinforcement Learning and Genetic Algorithms for Enhanced Optimization of Sustainability Practices in AI Systems

Abstract

This research paper explores the convergence of reinforcement learning (RL) and genetic algorithms (GA) as an innovative approach to optimize sustainability practices in artificial intelligence (AI) systems. As AI technologies grow in prevalence and complexity, their environmental impact, particularly energy consumption and carbon footprint, has become increasingly significant. The study introduces a hybrid framework that harnesses the adaptive capabilities of RL and the robust search mechanisms of GA to identify and implement sustainable strategies in AI operations. Through this framework, RL is utilized to dynamically adjust AI system parameters in response to environmental performance metrics, while GA aids in evolving these parameters to discover optimal configurations. Extensive simulations demonstrate that the proposed method substantially reduces energy consumption and carbon emissions compared to traditional optimization techniques. Additionally, the paper highlights the framework's potential to generalize across different AI systems and applications, suggesting a pathway toward universally sustainable AI development. The results indicate a promising direction for integrating AI with sustainability, potentially setting a benchmark for future research and practice in designing environmentally responsible AI technologies.