  This paper explores the use of the standard approach for proving runtime
bounds in discrete domains---often referred to as drift analysis---in the
context of optimization on a continuous domain. Using this framework we analyze
the (1+1) Evolution Strategy with one-fifth success rule on the sphere
function. To deal with potential functions that are not lower-bounded, we
formulate novel drift theorems. We then use the theorems to prove bounds on the
expected hitting time to reach a certain target fitness in finite dimension
$d$. The bounds are akin to linear convergence. We then study the dependency of
the different terms on $d$ proving a convergence rate dependency of
$\Theta(1/d)$. Our results constitute the first non-asymptotic analysis for the
algorithm considered as well as the first explicit application of drift
analysis to a randomized search heuristic with continuous domain.
