Open AccessJournal Article
Improved genetic algorithm for knapsack problem
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TL;DR: Experiments on knapsack problem illustrate that the new proposed genetic algorithm has better convergence, stability and efficiency than the current canonical genetic algorithm.
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Abstract: The paper discusses the premature convergence of canonical genetic algorithm.A parameter is introduced to weigh the chromosome similarity and increase the population diversity of the algorithm.The idea of simulated annealing is used to accept the new individual in the crossover and mutation operation.A new mutation operation is provided to improve the search efficiency.Experiments on knapsack problem illustrate that the new proposed genetic algorithm has better convergence,stability and efficiency.
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Citations
An Improved Quantum-Behaved Particle Swarm Optimization Algorithm for the Knapsack Problem
Xin Ran Li
- 01 Aug 2013
TL;DR: Improved Quantum-behaved particle swarm optimization algorithm has better convergence and stability in solving knapsack problem and slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome.
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A Multistart Local Search Heuristic for Knapsack Problem
Geng Lin
- 01 Jan 2013
TL;DR: This paper proposes a multistart local search heuristic for solving the knapsack problem, and the computational results show that the proposed algorithm can find high quality solutions in an effective manner.
An Improved Quantum-Behaved Particle Swarm Optimization Algorithm for the Knapsack Problem
Xin Ran Li
- 01 Aug 2013
TL;DR: An improved Quantum-behaved particle swarm optimization algorithm is proposed for 0-1 knapsack problem that has better convergence and stability in solving knapsack problem.