Solving unbounded knapsack problem using an adaptive genetic algorithm with elitism strategy

Authors:
Rung-Ching Chen;Cheng-Huei Jian
Affiliations:
Department of Information Management, Chaoyang University of Technology, Taiwan, Republic of China;Department of Information Management, Chaoyang University of Technology, Taiwan, Republic of China
Venue:
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
Year:
2007

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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.