Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
A class of generalized greedy algorithms for the multi-knapsack problem
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Computational Optimization and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Computing surrogate constraints for multidimensional Knapsack problems using evolution strategies
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
A flipping local search genetic algorithm for the multidimensional 0-1 knapsack problem
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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In this paper we present an evolutionary algorithm for the multidimensional 0–1 knapsack problem. Our algorithm incorporates a heuristic operator which computes problem-specific knowledge. The design of this operator is based on the general technique used to design greedy-like heuristics for this problem, that is, the surrogate multipliers approach of Pirkul (see [7]). The main difference with work previously done is that our heuristic operator is computed following a genetic strategy -suggested by the greedy solution of the one dimensional knapsack problem- instead of the commonly used simplex method. Experimental results show that our evolutionary algorithm is capable of obtaining high quality solutions for large size problems requiring less amount of computational effort than other evolutionary strategies supported by heuristics founded on linear programming calculation of surrogate multipliers.