This document discusses methods for optimizing query performance in a query optimizer called Scope by selecting alternative rule configurations. It proposes using rule signatures to group similar queries and generate candidate rule configurations to execute for each group. A learning model is then trained on execution results to select the best configuration for future queries in each group. The goal is to improve upon the default configuration by adapting to workloads and addressing inaccuracies in cardinality estimation that can lead to suboptimal plans.