Approximate Implementations of Pure Random Search in the Presence of Noise

Authors:
David L. Alexander;David W. Bulger;James M. Calvin;H. Edwin. Romeijn;Ryan L. Sherriff
Affiliations:
College of Sciences, Massey University, Wellington, New Zealand;Department of Statistics, Macquarie University, Australia 2109;Department of Computer and Information Science, New Jersey Institute of Technology, New York, U.S.A.;Department of Industrial and Systems Engineering, University of Florida, Gainesville, U.S.A.;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand
Venue:
Journal of Global Optimization
Year:
2005

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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.