  We propose a general formulation of a univariate estimation-of-distribution
algorithm (EDA). It naturally incorporates the three classic univariate EDAs
\emph{compact genetic algorithm}, \emph{univariate marginal distribution
algorithm} and \emph{population-based incremental learning} as well as the
\emph{max-min ant system} with iteration-best update. Our unified description
of the existing algorithms allows a unified analysis of these; we demonstrate
this by providing an analysis of genetic drift that immediately gives the
existing results proven separately for the four algorithms named above. Our
general model also includes EDAs that are more efficient than the existing ones
and these may not be difficult to find as we demonstrate for the OneMax and
LeadingOnes benchmarks.
