Strategy Selection

Strategy selection focuses on dynamically choosing the optimal approach from a set of alternatives to maximize performance in a given context. Current research emphasizes adaptive frameworks that leverage machine learning, such as genetic algorithms and XGBoost, to learn optimal strategy combinations or to switch between strategies based on real-time problem characteristics (e.g., task complexity, opponent behavior, data imbalance). These advancements improve efficiency and performance across diverse applications, including fault prediction, large language model reasoning, automated negotiation, and portfolio management, demonstrating the broad impact of intelligent strategy selection.