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Solving The Imprecise Weight Coefficients Knapsack Problem by Genetic Algorithms

This paper investigates solving the imprecise weight coefficients knapsack problem by genetic algorithms. We investigate the possibility of using genetic algorithms solving the fuzzy knapsack problem without defining membership functions for each imprecise weight coefficient. The proposed approach simulates a fuzzy number by distributing it into some partition points. We use genetic algorithms to evolve the values in each partition point so that the final values represent the membership grade of a fuzzy number. The fuzzy concept of the genetic algorithms approach is different, but gives better results than the traditional fuzzy approach.