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).

翻译：考虑在存在约束条件下优化离散参数的问题。我们采用带温度的随机sigmoid函数，并提出新的自适应梯度方法CONGA。通过个体种群来搜索最优解。每个个体根据“环境”梯度进行变化，并由两个具有不同退火调度方案的温度参数表征。未适应环境的个体死亡，而最优个体进行杂交，从而产生定向进化动力学。通过著名的背包最优填充组合问题（0-1 KP）对所述方法进行了说明。