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《Computer Engineering and Design》 2011-06
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Improved crowding genetic algorithm for 0-1 knapsack problem

LIU Wen-tao1,HU Jia-bao2(1.Department of Computer and Information Engineering,Wuhan Polytechnic University,Wuhan 430023,China; 2.College of Computer Science and Technology,Wuhan University of Technology,Wuhan 430070,China)  
Two kinds of improved crowding genetic algorithm for 0-1 knapsack problem are proposed,PFCGA and GCGA.PFCGA uses the penalty function and crowding genetic operation to get the optimal solution more stably.GCGA combines the greedy algorithm with crowding genetic.GCGA repairs the infeasible solution expressed by the illegal chromosome in the population and transform it to the feasible solution.GCGA modifies the non-optimal feasible solution and make it approach the optimal solution as much as possible.GCGA can get both high speed and high solution accuracy.According to the experiment simulation and comparative analysis,the test results demonstrate that the PFCGA and GCGA can get high solution accuracy and correct rate and they are rather efficient for solving 0-1 knapsack problem.
【CateGory Index】: TP301.6
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