  The problem is considered of optimizing discrete parameters in the presence
of constraints. We use the stochastic sigmoid with temperature and put forward
the new adaptive gradient method CONGA. The search for an optimal solution is
carried out by a population of individuals. Each of them varies according to
gradients of the 'environment' and is characterized by two temperature
parameters with different annealing schedules. Unadapted individuals die, and
optimal ones interbreed, the result is directed evolutionary dynamics. The
proposed method is illustrated using the well-known combinatorial problem for
optimal packing of a backpack (0-1 KP).
