随机故障下单机鲁棒调度算法的遗传编程方法

研究了随机故障环境下具有预测能力的单机鲁棒调度方法.通过插入空闲时段的方法吸收随机故障的扰动,进而对带空闲时段的鲁棒调度启发式,采用基于双子树结构编码的遗传编程体系加以学习.实验表明 所进化的启发式算法的拖期性能明显优于现有启发式,并通过适量插入空闲时段保持了较好的预测性能.这些算法由自适应的组合排序规则和空闲时段计算程序有机构成,并可较好地移植到其他不确定环境中.因此,所提出的遗传编程方法是不确定调度环境下相当有效的机器学习方法.     

中职建筑CAD课堂教学中的信息化技术应用

信息化技术在教学中的合理运用,不仅有利于学生对知识的掌握和自身能力的提高,而且有利于教师职业能力的提升。文章以中职建筑CAD教学为例,论述了信息化技术在课堂教学中的应用优势,解决了中职学生厌学、懒学、难学等问题。     

Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling

Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dynamic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.