Solving the Stock Reduction Problem with the Genetic Linear Programming Algorithm

Both Genetic Algorithm (GA) and Linear Programming (LP) are effective optimization algorithms. LP is very efficient for optimizing linear problems. GA can attain very good solutions for integer non-linear problems, but it takes more time. To solve the very complex nested optimization problems, we propose a hybrid algorithm to combine the merits from both LP and GA algorithms in this paper. We use GA to optimize the parent problem, and LP/GA hybrid algorithm to solve the sub problem. The Stock Reduction Problem (SRP) is a typical example of complex nested optimization problems. Our experiments have shown that our new hybrid algorithm can solve the SRP very fast with excellent results.