This paper deals with the optimization of an autogenous grinding circu
it using a random search technique. This technique is based on a hiera
rchical structure of learning automata operating in a random environme
nt constituted by the autogenous circuit to be optimized. The ore feed
rare to the mill is considered as the control variable while the mass
flow rate of the concentrate of the subsequent separation process con
stitutes the controlled variable. The variation domain of the manipula
ted variables is discretized into a set of regions which are associate
d to the actions of the automata of the last level of the hierarchical
learning system. A probability is associated to each action (region).
The learning system selects one of the available actions and, based o
n the response of the environment, modifies the strategy (the probabil
ities associated to the set of actions) using an adaptation procedure
called reinforcement scheme.