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- W2018443567 abstract "This paper presents an analysis of an adaptive random search (ARS) algorithm, a global minimization method. A probability model is introduced to characterize the statistical properties of the number of iterations required to find an acceptable solution. Moreover, based on this probability model, a new stopping criterion is introduced to predict the maximum number of iterations required to find an acceptable solution with a pre-specified level of confidence. This leads to the Monte Carlo version of the algorithm. Finally, this paper presents a systematic procedure for choosing the user-specified parameters in the ARS algorithm for fastest convergence. The results, which are valid for search spaces of arbitrary dimensions, are illustrated on a simple 3-dimensional example." @default.
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- W2018443567 date "2004-01-01" @default.
- W2018443567 modified "2023-09-27" @default.
- W2018443567 title "Analysis and parameter selection for an Adaptive Random Search algorithm" @default.
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- W2018443567 doi "https://doi.org/10.1109/cdc.2004.1429654" @default.
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