Genetic Algorithm Based on Greedy Strategy in the 0-1 Knapsack Problem
Jiangfei Zhao, TingLei Huang, Fei Pang, Yuanjie Liu
Abstract
Jiangfei Zhao, TingLei Huang, Fei Pang, Yuanjie Liu
Abstract
0-1 knapsack problem is a typical NP complex issues in field of computer. Traditional solve knapsack problem is recursively backtracking and greedy methods. Use recursive backtracking to solve knapsack problem algorithm of the advantages of thinking is that it simple and it can completely traverse the search space, sure to find the optimal solution but the solution space is exponential growth, when the large to a certain extent, with This algorithm will solve the knapsack problem is unrealistic. The greedy algorithm can only be obtained Approximate solve in a certain range near the optimal solution. In this paper, based on 0-1 knapsack problem is given a mathematical model, and analysis of the greedy strategy .we give agenetic algorithm to solve the knapsack problem. Greedy strategy combining the traditional genetic algorithm has been improved and shortened the time to solve, and to improve the accuracy of the solution.
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0-1 knapsack problem is a typical NP complex issues in field of computer. Traditional solve knapsack problem is recursively backtracking and greedy methods. Use recursive backtracking to solve knapsack problem algorithm of the advantages of thinking is that it simple and it can completely traverse the search space, sure to find the optimal solution but the solution space is exponential growth, when the large to a certain extent, with This algorithm will solve the knapsack problem is unrealistic. The greedy algorithm can only be obtained Approximate solve in a certain range near the optimal solution. In this paper, based on 0-1 knapsack problem is given a mathematical model, and analysis of the greedy strategy .we give agenetic algorithm to solve the knapsack problem. Greedy strategy combining the traditional genetic algorithm has been improved and shortened the time to solve, and to improve the accuracy of the solution.
Key concepts: Knapsack problem, Continuous knapsack problem, Greedy algorithm, Backtracking, Change-making problem, Mathematical optimization, Cutting stock problem, Genetic algorithm