On the choice of statistical model for one-dimensional P-algorithms

Document Type

Article

Publication Date

12-1-2000

Abstract

Algorithms based on statistical models compete favorably with other global optimization algorithms as proved by extensive testing results. Recently, techniques were developed for theoretically estimating the rate of convergence of global optimization algorithms with respect to the underlying statistical models. In the present paper these techniques are extended for theoretical investigation of P-algorithms without respect to a statistical model. Theoretical estimates may eliminate the need for lengthy experimental investigation which previously was the only method for comparison of the algorithms. The results obtained give new insight into the role of the underlying statistical model with respect to the asymptotic properties of the algorithm which will be useful for the implementation of new versions of the algorithms.

Identifier

0348000960 (Scopus)

Publication Title

Control and Cybernetics

ISSN

03248569

First Page

554

Last Page

565

Issue

2

Volume

29

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