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The zero/one multiple knapsack problem and genetic algorithms
Abstract
A genetic algorithm, GENEsYs, is applied to an NP-complete problem, the 0/1 multiple knapsack problem. The partitioning of the search space resulting from this highly constrained problem may include substantially large infeasible regions. Our implementation allows for the breeding and participation of infeasible strings in the population. Unlike many other GA-based algorithms that are augmented with domainspecific knowledge, GENEsYs uses a simple fitness function that uses a graded penalty term to penalize infeasibly bred strings. We apply our genetic algorithm to problem instances from the literature of well known test problems and report our experimental results. These encouraging results, especially for relatively large test problems, indicate that genetic algorithms can be successfully used as heuristics for finding good solutions for highly constrained NP-complete problems.
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Cited by
Error Thresholds in Genetic Algorithms
Ochoa, G. (2006) · Evolutionary Computation · doi:10.1162/evco.2006.14.2.157 · Cited by 71
Metadata
- Authors
- Khuri, S., Bäck, T., & Heitkötter, J.
- Venue
- Proceedings of the 1994 ACM symposium on Applied computing - SAC '94
- Publisher
- ACM Press
- Published
- 1994
- Pages
- 188-193
- OpenAlex
- W2040147130
- Cited by
- 259
- References
- 0
- FWCI
- 20.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The zero/one multiple knapsack problem and genetic algorithms. https://app.cochat.ai/papers/10.1145/326619.326694
- The paper
- Khuri, S., Bäck, T., & Heitkötter, J. (1994). The zero/one multiple knapsack problem and genetic algorithms. Proceedings of the 1994 ACM symposium on Applied computing - SAC '94. https://doi.org/10.1145/326619.326694
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