Abstract
We set up a general generic framework for local search algorithms. Then we show in this generic setting how heuristic, problem-specific information can be used to improve the success probability of local search by focussing the search process on specific neighbor states. Our main contribution is a result which states that stochastic local search using restarts has a provable complexity advantage compared to deterministic local search. An important side aspect is the insight that restarting (starting the search process all over, not using any information computed before) is a useful concept which was mostly ignored before.
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Schöning, U. (2007). Principles of Stochastic Local Search. In: Akl, S.G., Calude, C.S., Dinneen, M.J., Rozenberg, G., Wareham, H.T. (eds) Unconventional Computation. UC 2007. Lecture Notes in Computer Science, vol 4618. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73554-0_17
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DOI: https://doi.org/10.1007/978-3-540-73554-0_17
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