Khulood Alyahya
ABSTRACT
This paper studies two landscapes of different instances of the 0-1 knapsack problem. The instances are generated randomly from varied weight distributions. We show that the variation of the weights can be used to guide the selection of the most suitable local search operator for a given instance.
- K. Alyahya and J. E. Rowe. Local optima and weight distribution in the number partitioning problem. In PPSN XIII, volume 8672, pages 862--871. 2014.Google Scholar
Cross Ref
- J. Gottlieb. On the feasibility problem of penalty-based evolutionary algorithms for knapsack problems. In Applications of Evolutionary Computing, volume 2037, pages 50--59. 2001. Google ScholarDigital Library
- Z. Michalewicz and J. Arabas. Genetic algorithms for the 0/1 knapsack problem. In Methodologies for Intelligent Systems, volume 869, pages 134--143. 1994. Google ScholarDigital Library
- D. Pisinger. Where are the hard knapsack problems? Comput. Oper. Res., 32(9):2271--2284, 2005. Google ScholarDigital Library
- P. F. Stadler. Fitness landscapes. In Biological Evolution and Statistical Physics, volume 585, pages 183--204. 2002.Google ScholarCross Ref
Index Terms
- Landscape Properties of the 0-1 Knapsack Problem
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