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Theoretical and empirical evidence exists that the hierarchical Bayesian optimization algorithm (hBOA) can solve challenging hierarchical problems and anything easier. This paper applies hBOA to two important classes of real-world problems: Ising spin-glass systems and maximum satisfiability (MAXSAT). The paper shows how easy it is to apply hBOA to real-world optimization problems--in most cases hBOA can be applied without any prior problem analysis, it can acquire enough problem-specific knowledge automatically. The results indicate that hBOA is capable of solving enormously difficult problems that cannot be solved by other optimizers and still provide competitive or better performance than problem-specific approaches on other problems. The results thus confirm that hBOA is a practical, robust, and scalable technique for solving challenging real-world problems.