Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Improved genetic algorithm for the permutation flowshop scheduling problem
Computers and Operations Research
A genetic algorithm for the quadratic multiple knapsack problem
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Generalized quadratic multiple knapsack problem and two solution approaches
Computers and Operations Research
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In this paper, a genetic algorithm (GA) is developed for solving 0-1 knapsack problems (KPs) and performance of the GA is optimized using Taguchi method (TM). In addition to population size, crossover rate, and mutation rate, three types of crossover operators and three types of reproduction operators are taken into account for solving different 0-1 KPs, each has differently configured in terms of size of the problem and the correlation among weights and profits of items. Three sizes and three types of instances are generated for 0-1 KPs and optimal values of the genetic operators for different types of instances are investigated by using TM. We discussed not only how to determine the significantly effective parameters for GA developed for 0-1 KPs using TM, but also trace how the optimum values of the parameters vary regarding to the structure of the problem.