The paper "Sequential Model-Based Optimization for General Algorithm Configuration (extended version)" by Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown addresses the challenge of configuring algorithms for hard computational problems, which often have many parameters that can be adjusted to improve performance. The authors propose a new approach that extends the capabilities of sequential model-based optimization (SMBO) to handle general algorithm configuration problems, including multiple categorical parameters and sets of benchmark instances. They introduce two novel methods: Random Online Aggressive Racing (ROAR) and Sequential Model-based Algorithm Configuration (SMAC). ROAR is a simple, model-free method that selects configurations randomly and aggressively, while SMAC uses a more sophisticated model-based approach. The methods are evaluated on various problems, including local search and tree search solvers for propositional satisfiability (SAT) and the commercial mixed integer programming (MIP) solver CPLEX. The experiments show that SMAC and ROAR outperform existing state-of-the-art configuration approaches in many scenarios, demonstrating the effectiveness of their proposed methods.The paper "Sequential Model-Based Optimization for General Algorithm Configuration (extended version)" by Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown addresses the challenge of configuring algorithms for hard computational problems, which often have many parameters that can be adjusted to improve performance. The authors propose a new approach that extends the capabilities of sequential model-based optimization (SMBO) to handle general algorithm configuration problems, including multiple categorical parameters and sets of benchmark instances. They introduce two novel methods: Random Online Aggressive Racing (ROAR) and Sequential Model-based Algorithm Configuration (SMAC). ROAR is a simple, model-free method that selects configurations randomly and aggressively, while SMAC uses a more sophisticated model-based approach. The methods are evaluated on various problems, including local search and tree search solvers for propositional satisfiability (SAT) and the commercial mixed integer programming (MIP) solver CPLEX. The experiments show that SMAC and ROAR outperform existing state-of-the-art configuration approaches in many scenarios, demonstrating the effectiveness of their proposed methods.