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
Fundamentals of algorithmics
The zero/one multiple knapsack problem and genetic algorithms
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Permutation-based evolutionary algorithms for multidimensional knapsack problems
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Heterogeneous cooperative coevolution: strategies of integration between GP and GA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
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It is an important task to obtain optimal solutions for multidimensional linear integer problems with multiple constraints. The surrogate constraint method translates a multidimensional problem into an one dimensional problem using a suitable set of surrogate multipliers. In general, there exists a gap between the optimal solution of the surrogate problem and the original multidimensional problem. Moreover, computing suitable surrogate constraints is a computationally difficult task. In this paper we propose a method for computing surrogate constraints of linear problems that evolves sets of surrogate multipliers coded in floating point and uses as fitness function the value of the Ɛ-approximate solution of the corresponding surrogate problem. This method allows the user to adjust the quality of the obtained multipliers by means of parameter Ɛ. Solving 0 - 1 multidimensional knapsack problems we test the effectiveness of our methodology. Experimental results show that our method for computing surrogate constraints for linear 0 - 1 integer problems is at least as effective as other strategies based on Linear Programming as that proposed by Chu and Beasley in [6].