Abstract
This paper analyses a random search algorithm for global optimization that allows acceptance of non-improving points with a certain probability. The algorithm is called discrete backtracking adaptive search. We derive upper and lower bounds on the expected number of iterations for the random search algorithm to first sample the global optimum. The bounds are derived by modeling the algorithm using a series of absorbing Markov chains. Finally, upper and lower bounds for the expected number of iterations to find the global optimum are derived for specific forms of the algorithm.
The work of these authors has been supported in part by NSF gram DM1-9820878 and the Marsden Fund administered by the Royal Society of New Zealand.
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© 2002 Kluwer Academic Publishers
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Kristinsdottir, B.P., Zabinsky, Z.B., Wood, G.R. (2002). Discrete Backtracking Adaptive Search for Global Optimization. In: Dzemyda, G., Šaltenis, V., Žilinskas, A. (eds) Stochastic and Global Optimization. Nonconvex Optimization and Its Applications, vol 59. Springer, Boston, MA. https://doi.org/10.1007/0-306-47648-7_9
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DOI: https://doi.org/10.1007/0-306-47648-7_9
Publisher Name: Springer, Boston, MA
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