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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2005.1
- 수록면
- 149 - 155 (7page)
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In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.Key-words : Real-coded genetic algorithm, Simulation optimization, Binary-coded genetic algorithm
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참고문헌 (14)
참고문헌 신청
/ / Genetic algorithms in search
/ / Sizing populations for serial and parallel genetic algorithms Proceedings of 3rd International Conference on Genetic Algorithms and Their Applications : 70 ~ 79
/ 1985. / Optimal initial population size for binary-coded genetic algorithms University of Alabama
/ 1990. / Genetic algorithms for real parameter optimization Foundations of Genetic Algorithms 1st Workshop on the Foundations of Genetic Algorithms and Classifier Systems :
/ 1991. / An Experimental comparison of binary and floating point representations in genetic algorithms Proceedings of 4th International Conference on Genetic Algorithms
/ / Sizing populations for serial and parallel genetic algorithms Proceedings of 3rd International Conference on Genetic Algorithms and Their Applications : 70 ~ 79
/ 1985. / Optimal initial population size for binary-coded genetic algorithms University of Alabama
/ 1990. / Genetic algorithms for real parameter optimization Foundations of Genetic Algorithms 1st Workshop on the Foundations of Genetic Algorithms and Classifier Systems :
/ 1991. / An Experimental comparison of binary and floating point representations in genetic algorithms Proceedings of 4th International Conference on Genetic Algorithms
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