Accurate forecasting of reference evapotranspiration (ETo) is crucial for sustainable water resource management and precision agriculture. The present study evaluates three ETo prediction methods: Random Forest (RF), Cartesian Genetic Programming (CGP), and Convolutional Neural Network-Graphics Processing Unit (CNN-GPU) across time intervals of 1 to 364 days. Using dispersion analysis (scatter/violin plots) and accuracy metrics (RMSE, MAE, R^2, SI), it was seen that the RF and CNN-GPU models consistently outperform CGP, particularly at extended horizons. At 364 days, CNN-GPU achieved the highest accuracy (RMSE: 0.678 mm/day, R^2: 0.874), while RF maintained robust performance (RMSE: 0.683 mm... More >

The growing prominence of prompt engineering as a means of controlling large language models has given rise to a diverse set of methods, ranging from handcrafted templates to embedding-level tuning. Yet, as prompts increasingly serve not merely as input scaffolds but as adaptive interfaces between users and models, the question of how to systematically optimize them remains unresolved. Reinforcement learning, with its capacity for sequential decision-making and reward-driven adaptation, has been proposed as a possible framework for discovering effective prompting strategies. This survey explores the emerging intersection of RL and prompt engineering, organizing existing research along three... More >