针对平面度误差计算的特点，提出了一种基于实数编码的改进遗传算法。
According to the characteristics of flatness error evaluation, an improved genetic algorithms based on real coding is proposed.
数值实验表明，带有适应值激励机制的改进遗传算法的搜索效率得到很大提高。
Experimental results show that GAs with fitness stimulating mechanism greatly improve the efficiency compared with conventional GAs.
对几种改进的遗传算法进行了比较、分析、综合后，提出了一种基于二进制编码的改进遗传算法。
To deal with the deficiency in symbol code and binary code in genetic neural network, a new hybrid code method is proposed on the basis of binary code.
该文提出的改进遗传算法改善了遗传算法中收敛概率低的问题，目标函数能满足客户对车间调度现实问题的需要。
The whole scheduling process of dynamic simulation can be observed, which proves the IGA feasible and efficient in practice. IGA could improve the problem of low probability of convergence.
针对这些问题，对基本遗传算法引入了邻域操作、自适应策略和混沌优化等多种改进策略，研究设计了一种有机结合各种改进策略的改进遗传算法流程。
To avoid these problems, some methods including adjacent-domain operations, adaptability and chaos have been taken into consideration in this paper to improve the capability of the algorithm.
实验结果表明，改进算法对提高遗传算法的运算速度是可行和有效的。
Experimental results show that improved genetic algorithms are effective and practicable to enhance the calculating velocity.
对于有时间窗的非满载VSP问题，将货运量约束和软时间窗约束转化为目标约束，建立了非满载VSP模型，设计了基于自然数编码，使用最大保留交叉、改进的反转变异等技术的遗传算法。
On the VSP with time window, while the restraints of capacity and time windows are changed into object restraints, a mathematic model is established.
文中还将改进的实数遗传算法用于测量数据的估计中，得到了较好的线性和非线性参数估计结果。
The new algorithm is also used in parameter estimate, and the results of linear estimate and nonlinear estimate all show the effective of the improved algorithm.
本文探讨了多目标遗传算法存在的问题，并提出了相应的改进策略。
This paper discusses some problems of Multi-objective Genetic Algorithms at the same time, gives some new improvements to MOGAs.
采用改进的遗传算法，求解了具有屈曲约束，尤其截面积是离散型的桁架拓扑优化。
We present an improved genetic algorithm(GA) for topology optimization of a truss with discrete sizing and under local buckling constraints.
该算法采用两种虚拟种群的方法对常规遗传算法及浮点数遗传算法的种群实行改进。
The advanced GA USES two virtual population methodologies to process the population of standard binary and real code GA for RPO problem.
该算法采用两种虚拟种群的方法对常规遗传算法及浮点数遗传算法的种群实行改进。
The advanced GA USES two virtual population methodologies to process the population of standard binary and real code GA for RPO problem.
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