Conference Paper

A genetic algorithm for the unbounded knapsack problem

Sch. of Comput., Huazhong Univ. of Sci. & Technol., Wuhan, China
DOI: 10.1109/ICMLC.2003.1259749 In proceeding of: Machine Learning and Cybernetics, 2003 International Conference on, Volume: 3
Source: IEEE Xplore

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.

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