  In this paper, we develop a simulation-based approach to optimisation with
multi-modal functions using slice sampling. Our method specifies the objective
function as an energy potential in a Boltzmann distribution and then we use
auxiliary exponential slice variables to provide samples for a variety of
energy levels. Our slice sampler draws uniformly over the augmented slice
region. We identify the global modes by projecting the path of the chain back
to the underlying space. Four standard test functions are used to illustrate
the methodology: Rosenbrock, Himmelblau, Rastrigin, and Shubert. These
functions demonstrate the flexibility of our approach as they include functions
with long ridges (Rosenbrock), multi-modality (Himmelblau, Shubert) and many
local modes dominated by one global (Rastrigin). The methods described here are
implemented in the {\tt R} package {\tt McmcOpt}.
