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).

优化离散参数存在约束问题，我们使用带有温度的随机 S 型函数，并提出了新的自适应梯度方法 CONGA。通过一个个体群体来搜索最优解，每个个体根据 ' 环境 ' 的梯度变化，并由具有不同退火进程的两个温度参数来描述。未经适应的个体消亡，而最优个体繁殖，其结果是定向的进化动力学。我们利用著名的背包完全装配（0-1 KP）问题来说明所提出的方法。