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Stochastic local search; Noise; Markov chain models; Expected hitting times; Rational functions; Noise response curves; Probabilistic reasoning; Bayesian networks; Most probable explanation; Systematic experiments; Polynomial approximation; Convexity
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Understanding The Role Of Noise In Stochastic Local Search: Analysis And Experiments, Ole J. Mengshoel
Understanding The Role Of Noise In Stochastic Local Search: Analysis And Experiments, Ole J. Mengshoel
Ole J Mengshoel
Stochastic local search (SLS) algorithms have recently been proven to be among the best approaches to solving computationally hard problems. SLS algorithms typically have a number of parameters, optimized empirically, that characterize and determine their performance. In this article, we focus on the noise parameter. The theoretical foundation of SLS, including an understanding of how to the optimal noise varies with problem difficulty, is lagging compared to the strong empirical results obtained using these algorithms. A purely empirical approach to understanding and optimizing SLS noise, as problem instances vary, can be very computationally intensive. To complement existing experimental results, we …