Size of Neighborhood More Important than Temperature for Stochastic Local Search
نویسندگان
چکیده
In the paper we investigate stochastic local search by Markov chain analysis in a high and a low dimensional discrete space. In the n-dimensional space Bn a function called Jump is considered. The analysis shows that an algorithm using a large neighborhood and never accepting worse points performs much better than any local search algorithm accepting worse points with a certain probability. We also investigate functions in the space Bn with many local optima. Here we compare stochastic local search using large neigborhoods with a local search using optimal temperature schedules which depend on the state of the Markov process.
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