Abstract
We discuss the noisy optimisation problem, in which function evaluations are subject to random noise. Adaptation of pure random search to noisy optimisation by repeated sampling is considered. We introduce and exploit an improving bias condition on noise-affected pure random search algorithms. Two such algorithms are considered; we show that one requires infinite expected work to proceed, while the other is practical.
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References
M.H. Alrefaei S. Andradóttir (1996) ArticleTitleA simulated annealing algorithm with constant temperature for discrete stochastic optimization Management Science 45 IssueID5 748–764
R.B. Ash C.A. Doléans-Dade (2000) Probability and Measure Theory Academic Press San Diego, California
Baumert S., Smith R.L. (2001). Pure Random Search for Noisy Objective Functions. University of Michigan Technical Report 01–03.
C.G.E. Boender H.E. Romeijn (1995) Stochastic methods R. Horst P.M. Pardalos (Eds) Handbook of Global Optimization Kluwer Academic Publishers Dordrecht, The Netherlands 829–869
S.H. Brooks (1958) ArticleTitleA discussion of random methods for seeking maxima Operations Research 6 244–251
L. Devroye (1978) ArticleTitleProgressive global random search of continuous functions Mathematical Programming 15 330–342
L.P. Devroye (1976) ArticleTitleOn the convergence of statistical search IEEE Transactions on Systems, Man, and Cybernetics 6 IssueID1 46–56
B.K. Ghosh P.K. Sen (Eds) (1991) Handbook of Sequential Analysis Dekker New York
L.S. Gurin (1966) ArticleTitleRandom search in the presence of noise Engineering Cybernetics 4 IssueID3 252–260
D. Siegmund (1985) Sequential Analysis Springer-Verlag New York
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Supported by a Bright Future Scholarship administered by the Foundation for Research, Science and Technology, New Zealand
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Alexander, D.L.J., Bulger, D.W., Calvin, J.M. et al. Approximate Implementations of Pure Random Search in the Presence of Noise. J Glob Optim 31, 601–612 (2005). https://doi.org/10.1007/s10898-004-9970-4
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DOI: https://doi.org/10.1007/s10898-004-9970-4