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Greedy genetic algorithm for solving knapsack problems and its applications

Yichao He, Zhang Cui-jun

Open publisher page 15 citations

Abstract

The flaw of greedy transform method adopted by hybrid henetic algorithm(HGA)is analyzed,which is a effective algorithm to solve knapsack problem in Ref.[2].A new define on greedy transform is redefined and a new and more effective implement method is advanced.The new method is combined with genetic algorithm to propose a new hybrid genetic algorithm that is greedy genetic algo- rithm(GGA).Using GGA,a best solution of famous knapsack sample is found in Ref.[2,4] at present.Moreover,for a knapsack sample in Ref.[7] and a randomly generated knapsack sample,the calculation results using GGA and HGA show that the global con- vergence of GGA is much more superior to HGA.

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What this paper is about

The flaw of greedy transform method adopted by hybrid henetic algorithm(HGA)is analyzed,which is a effective algorithm to solve knapsack problem in Ref.[2].A new define on greedy transform is redefined and a new and more effective implement method is advanced.The new method is combined with genetic algorithm to propose a new hybrid genetic algorithm that is greedy genetic algo- rithm(GGA).Using GGA,a best solution of famous knapsack sample is found in Ref.[2,4] at present.Moreover,for a knapsack sample in Ref.[7] and a randomly generated knapsack sample,the calculation results using GGA and HGA show that the global con- vergence of GGA is much more superior to HGA.

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Available abstract

The flaw of greedy transform method adopted by hybrid henetic algorithm(HGA)is analyzed,which is a effective algorithm to solve knapsack problem in Ref.[2].A new define on greedy transform is redefined and a new and more effective implement method is advanced.The new method is combined with genetic algorithm to propose a new hybrid genetic algorithm that is greedy genetic algo- rithm(GGA).Using GGA,a best solution of famous knapsack sample is found in Ref.[2,4] at present.Moreover,for a knapsack sample in Ref.[7] and a randomly generated knapsack sample,the calculation results using GGA and HGA show that the global con- vergence of GGA is much more superior to HGA.

Key concepts: Knapsack problem, Continuous knapsack problem, Greedy algorithm, Genetic algorithm, Computer science, Change-making problem, Algorithm, Mathematical optimization

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