Bryant A. Julstrom
ABSTRACT
The Quadratic Knapsack Problem extends the 0-1 Knapsack Problem by associating values not only with individual objects but also with pairs of objects. Two genetic algorithms encode candidate solutions to this problem as permutations of objects. One GA applies no heuristic steps while a second seeds its population and performs crossover by considering the values of objects relative to the objects already in the knapsack. Both algorithms perform well, and competitively with two earlier binary-coded GAs. The heuristic measures improve performance significantly.
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Cross Ref
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- B. A. Julstrom. Greedy, genetic, and greedy genetic algorithms for the Quadratic Knapsack Problem. In H.-G. Beyer et al., editors, Proceedings of the 2005 Genetic and Evolutionary Computation Conference, GECCO-2005, volume 1, pages 607--614, New York, 2005. ACM Press. Google ScholarDigital Library
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Index Terms
- Naive and heuristic permutation-coded genetic algorithms for the quadratic knapsack problem
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