Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution
This work addresses combinatorial optimization challenges for researchers and practitioners, but it appears incremental as it builds on existing gradient and evolutionary methods.
The paper tackles the problem of optimizing discrete parameters under constraints by proposing a new adaptive gradient method called CONGA, which combines gradient-based variation with directed evolutionary dynamics, and demonstrates its effectiveness on the 0-1 knapsack problem with improved performance metrics.
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).