Landscape Regularity and Random Walks for the Job-Shop Scheduling Problem

We perform a novel analysis of the fitness landscape of the job-shop scheduling problem (JSP). In contrast to other well-known combinatorial optimization problems, we show that the landscape of the JSP is non-regular, in that the connectivity of solutions is variable. As a consequence, we argue that random walks performed on such a landscape will be biased. We conjecture that such a bias should affect both random walks and local search algorithms, and may provide a partial explanation for the remarkable success of the latter in solving the JSP.

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