A genetic algorithm for the unbounded knapsack problem
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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ABSTRACT: A new algorithm for the generalised assignment problem is described in this paper. The algorithm is adapted from a genetic algorithm which has been successfully used on set covering problems, but instead of genetically improving a set of feasible solutions it tries to genetically restore feasibility to a set of near-optimal ones. Thus it may be regarded as operating in a dual sense to the more familiar genetic approach. The algorithm has been tested on generalised assignment problems of substantial size and compared to an exact integer programming approach and a well-established heuristic approach.Journal of the Operational Research Society 07/1997; 48(8):804-809. · 0.99 Impact Factor
- 01/1990; Wiley.
- 01/1979; W. H. Freeman and Company.