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1997

Comparison of Monte Carlo and Deterministic Methods for Non-Adaptive Optimization

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Comparison of Monte Carlo and Deterministic Methods for Non-Adaptive Optimization
In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.
Hisham A. Al-Mharmah, James M. Calvin
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where WSC
Authors Hisham A. Al-Mharmah, James M. Calvin
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