  Stochastic optimization is an important task in many optimization problems
where the tasks are not expressible as convex optimization problems. In the
case of non-convex optimization problems, various different stochastic
algorithms like simulated annealing, evolutionary algorithms, and tabu search
are available. Most of these algorithms require user-defined parameters
specific to the problem in order to find out the optimal solution. Moreover, in
many situations, iterative fine-tunings are required for the user-defined
parameters, and therefore these algorithms cannot adapt if the search space and
the optima changes over time. In this paper we propose an \underline{a}daptive
parameter-free \underline{s}tochastic \underline{o}ptimization technique for
\underline{c}ontinuous random variables called ASOC.
