This paper presents a new genetic algorithm for job-shop scheduling problem. Based on schema theorem and building block hypothesis, a new crossover is proposed. By selecting short, low-order, highly fit schemas for ge...This paper presents a new genetic algorithm for job-shop scheduling problem. Based on schema theorem and building block hypothesis, a new crossover is proposed. By selecting short, low-order, highly fit schemas for genetic operator, the crossover can maintain a diversity of population without disrupting the characteristics and search the global optimization. Simulation results on famous benchmark problems MT06, MT10 and MT20 coded by Matlab show that our genetic operators are suitable to job-shop scheduling problems and outperform the previous GA-based approaches.展开更多
In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Usi...In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Using a genetic algorithm, this paper seeks to find the optimal parameters varied with different sub images to compute the adaptive segmentation threshold.The experimental results reveal that the GA paradigm is an efficient and effective method of search.展开更多
In this paper, we propose a new genetic algorithm for job-shop scheduling problems （JSP）. The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new cro...In this paper, we propose a new genetic algorithm for job-shop scheduling problems （JSP）. The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new crossover is proposed ： By selecting short, low order highly fit schemas to genetic operator, the crossover can exchange meaningful ordering information of parents effectively and can search the global optimization. Simulation results on MT benchmark problem coded by C ＋ ＋ show that our genetic operators are very powerful and suitable to job-shop scheduling problems and our method outperforms the previous GA-based approaches.展开更多
针对遗传算法在理论研究方面存在的不足 ,系统地讨论了遗传算法理论研究的主要内容和方法 ,包括模式定理、编码策略、Markov链与全局收敛性、维数分析、BGA理论、可分离函数、Walsh与傅立叶函数分析及二次动力系统等 ,介绍了 No Free L ...针对遗传算法在理论研究方面存在的不足 ,系统地讨论了遗传算法理论研究的主要内容和方法 ,包括模式定理、编码策略、Markov链与全局收敛性、维数分析、BGA理论、可分离函数、Walsh与傅立叶函数分析及二次动力系统等 ,介绍了 No Free L unch定理 。展开更多
文摘This paper presents a new genetic algorithm for job-shop scheduling problem. Based on schema theorem and building block hypothesis, a new crossover is proposed. By selecting short, low-order, highly fit schemas for genetic operator, the crossover can maintain a diversity of population without disrupting the characteristics and search the global optimization. Simulation results on famous benchmark problems MT06, MT10 and MT20 coded by Matlab show that our genetic operators are suitable to job-shop scheduling problems and outperform the previous GA-based approaches.
文摘In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Using a genetic algorithm, this paper seeks to find the optimal parameters varied with different sub images to compute the adaptive segmentation threshold.The experimental results reveal that the GA paradigm is an efficient and effective method of search.
文摘In this paper, we propose a new genetic algorithm for job-shop scheduling problems （JSP）. The proposed method uses the operation-based representation, based on schema theorem and building block hypothesis, a new crossover is proposed ： By selecting short, low order highly fit schemas to genetic operator, the crossover can exchange meaningful ordering information of parents effectively and can search the global optimization. Simulation results on MT benchmark problem coded by C ＋ ＋ show that our genetic operators are very powerful and suitable to job-shop scheduling problems and our method outperforms the previous GA-based approaches.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.69975010No.60374054)+1 种基金山东省自然科学基金(the Natural Science Foundation of Shandong Province of China under Grant No.Y2003G01No.Z2006G09)
