This dissertation presents the studies of genetic algorithms (GA) based model structure and parameters conjunct identification, PID optimization, and the engineering application method.
本论文是基于遗传算法(GA)的模型结构和参数共同辨识、PID参数的优化整定和工程应用方法的研究。
According to the characteristics of adaptability in GA, the optimization model of teaching assignment distribution is made and the scheme is carried out.
建立教学任务分配的优化模型，结合遗传算法自适应搜索优化解的特点，给出了其遗传算法实现的方案。
The GA-BP model is built by the nonlinear mapping ability of BP network and the global optimization of GA.
在GA - BP模型中主要是利用了BP网络的非线性映射能力和GA的全局优化能力。
A model for the optimization load allocation among power generating units is proposed by using fuzzy theory and solve it with the improved GA.
通过引入模糊理论提出了电厂机组负荷分配模型，并运用改进的遗传算法进行求解。
Based on logistics cost optimization, the procedure of SM-CC is analyzed, and a model of order grouping is suggested and performed by self-adaptated GA.
计算结果表明，混合钢铁流程炼钢-连铸 浇次组合与排序计划算法是有效的，该算法已运用于生产实际，具有推广价值。
Based on logistics cost optimization, the procedure of SM-CC is analyzed, and a model of order grouping is suggested and performed by self-adaptated GA.
计算结果表明，混合钢铁流程炼钢-连铸 浇次组合与排序计划算法是有效的，该算法已运用于生产实际，具有推广价值。
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