Abstract
The Knapsack problem is an NP-Complete problem. Unbounded Knapsack problems are more complex and harder to solve than the general Knapsack problem. In this paper, we apply the genetic algorithm to solve the unbounded Knapsack problem. We use an elitism strategy to overcome the defect of the slow convergence rate of the general genetic algorithm. The elitism strategy retains good chromosomes and ensures that they are not eliminated through the mechanism of crossover and mutation, while ensuring that the features of the offspring chromosomes are at least as good as their parents. The system automatically adapts the number of the initial population of chromosomes and the number of runs of the genetic algorithm. In addition, we use the strategy of greedy method to auto adaptive the sequence of chromosomes to enhance the effect of executing. Experimental results showed that our method could fast find the best solution of the problem.
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Chen, RC., Jian, CH. (2007). Solving Unbounded Knapsack Problem Using an Adaptive Genetic Algorithm with Elitism Strategy. In: Thulasiraman, P., He, X., Xu, T.L., Denko, M.K., Thulasiram, R.K., Yang, L.T. (eds) Frontiers of High Performance Computing and Networking ISPA 2007 Workshops. ISPA 2007. Lecture Notes in Computer Science, vol 4743. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74767-3_21
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DOI: https://doi.org/10.1007/978-3-540-74767-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74766-6
Online ISBN: 978-3-540-74767-3
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