Optimization Target

Optimization target selection is a crucial aspect of many machine learning applications, aiming to define the specific metric guiding the learning process towards improved performance. Current research focuses on refining optimization targets for diverse tasks, including code optimization (using search-based LLMs and evolutionary algorithms), anomaly detection (employing multiple scoring metrics for automated parameter tuning), and reinforcement learning (developing policies adaptable to variable objectives). These advancements improve efficiency, accuracy, and adaptability in various domains, impacting fields ranging from software engineering to industrial monitoring and control systems.