A flipping local search genetic algorithm for the multidimensional 0-1 knapsack problem

In this paper we present an evolutionary strategy for the multidimensional 0-1 knapsack problem. Our algorithm incorporates a flipping local search process in order to locally improve the obtained individuals and also, a heuristic operator which computes problem-specific knowledge, based on the surrogate multipliers approach introduced in [12]. Experimental results show that our evolutionary algorithm is capable of obtaining high quality solutions for large size problems and that the local search procedure significatively improves the final obtained result.