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The use of random test problems to evaluate algorithm performance raises an important, and generally unanswered, question: Are the results generalizable to more realistic problems? Researchers generally assume that algorithms with superior performance on difficult, random test problems will also perform well on more realistic, structured problems. Our research explores this assumption for the permutation flow-shop scheduling problem. We introduce a method for generating structured flow-shop problems, which are modeled after features found in some real-world manufacturing environments. We perform experiments that indicate significant differences exist between the search-space topologies of random and structured flow-shop problems, and demonstrate that these differencescanaffect the performance of certain algorithms. Yet despite these differences, and in contrast to difficult random problems, the …