Stochastic Global Optimization

This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; some of the topics include an introduction to global random search, statistical inference, several associated random search algorithms, and various approaches to statistical models.

This book aims to cover major methodological and theoretical developments in the ?eld of stochastic global optimization. This ?eld includes global random search and methods based on probabilistic assumptions about the objective function. We discuss the basic ideas lying behind the main algorithmic schemes, formulate the most essential algorithms and outline the ways of their theor- ical investigation. We try to be mathematically precise and sound but at the same time we do not often delve deep into the mathematical detail, referring instead to the corresponding literature. We often do not consider the most g- eral assumptions, preferring instead simplicity of arguments. For example, we only consider continuous ?nite dimensional optimization despite the fact that some of the methods can easily be modi?ed for discrete or in?nite-dimensional optimization problems. The authors' interests and the availability of good surveys on particular topics have in uenced the choice of material in the book. For example, there are excellent surveys on simulated annealing (both on theoretical and - plementation aspects of this method) and evolutionary algorithms (including genetic algorithms). We thus devote much less attention to these topics than they merit, concentrating instead on the issues which are not that well d- umented in literature. We also spend more time discussing the most recent ideas which have been proposed in the last few years.

Provides reader with a methodological and theoretical basis for developing and investigating optimization heuristics Summarizes basic ideas and presents recent progress and new results Includes an extensive bibliography with old Russian articles as well as new English papers Includes an extensive discussion on probabilistic and statistical models used in the global random search Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and populations based algorithms Includes supplementary material: sn.pub/extras

Contenu
Basic Concepts and Ideas.- Global Random Search: Fundamentals and Statistical Inference.- Global Random Search: Extensions.- Methods Based on Statistical Models of Multimodal Functions.