A genetic algorithm for the unbounded knapsack problem

Conference Paper · December 2003with14 Reads
DOI: 10.1109/ICMLC.2003.1259749 · Source: IEEE Xplore
Conference: Machine Learning and Cybernetics, 2003 International Conference on, Volume: 3

    Abstract

    In this paper a new evolutionary algorithm is presented for the unbounded knapsack problem, which is a famous NP-complete combinatorial optimization problem. The proposed genetic algorithm is based on two techniques. One is a heuristic operator, which utilizes problem-specific knowledge, and the other is a preprocessing technique. Computational results show that the proposed algorithm is capable of obtaining high-quality solutions for problems of standard randomly generated knapsack instances, while requiring only a modest amount of computational effort.