The field of performance tuning and optimization is moving towards more efficient and effective methods for improving system performance. Recent research has focused on developing new techniques for surrogate model selection, constraint acquisition, and co-evolutionary tuning. These approaches aim to reduce the complexity and cost of tuning processes, while also improving the accuracy and robustness of the resulting models. Notable papers in this area include: Unveiling Many Faces of Surrogate Models for Configuration Tuning, which proposes a systematic exploration of surrogate models for configuration tuning. Overcoming Over-Fitting in Constraint Acquisition via Query-Driven Interactive Refinement, which introduces a hybrid framework for constraint acquisition that addresses the challenge of over-fitting. CoTune: Co-evolutionary Configuration Tuning, which proposes a co-evolutionary approach to configuration tuning that takes into account complex performance requirements. DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments, which introduces a tournament-based design for performance tuning in shared, interference-prone cloud environments. Boolean Satisfiability via Imitation Learning, which proposes a branching policy for conflict-driven clause learning solvers based on imitation learning. Efficient Construction of Large Search Spaces for Auto-Tuning, which reformulates search space construction as a Constraint Satisfaction Problem and develops a runtime parser and optimized solver. Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning, which presents a novel method for hyperparameter tuning of optimization algorithms for auto-tuning. Relevance-Zone Reduction in Game Solving, which proposes an iterative method for reducing the relevance zone in game solving, resulting in smaller search spaces and improved pruning efficiency.