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.
