Journal:Informatica
Volume 27, Issue 2 (2016), pp. 323–334
Abstract
This paper reviews the interplay between global optimization and probability models, concentrating on a class of deterministic optimization algorithms that are motivated by probability models for the objective function. Some complexity results are described for the univariate and multivariate cases.
Journal:Informatica
Volume 27, Issue 2 (2016), pp. 229–256
Abstract
This is a survey of the main achievements in the methodology and theory of stochastic global optimization. It comprises two complimentary directions: global random search and the methodology based on the use of stochastic models about the objective function. The main attention is paid to theoretically substantiated methods and mathematical results proven in the last 25 years.
Journal:Informatica
Volume 22, Issue 4 (2011), pp. 471–488
Abstract
We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.