稀疏阵列天线综合的遗传算法优化

通过遗传算法优化选择常规满阵中一定比例的工作单元,使阵列在欠饱和状态下达到窄波束低副瓣的要求。以单元的工作状态和激励幅度为优化参量,波束宽度和副瓣电平为适值函数,通过适值缩放和多种群进化竞争使遗传进化稳定地收敛到全局最优。详细分析了稀疏阵的单元布局、稀疏率及单元激励对优化阵列特性的影响。     

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state machines is computationally hard.Most of published results show that the reduction of switching activity often trades with area penalty.In this paper,a new approach is proposed.Experimental results show a significant reduction of switching activity without area penalty compared with previous publications.     

紫杉醇及其类似物生产的代谢工程研究进展

大量生产抗癌新药紫杉醇是目前生物技术的研究热点之一。通过对紫杉醇生物合成途径中关键酶基因的分离、克隆与遗传转化等方面最新研究结果进行评述 ,指出通过紫杉醇次生代谢途径中关键酶的基因改造及遗传转化 ,构建高效表达紫杉醇或其重要前体紫杉烷的基因工程细胞株 ,并建立其相应的基因表达调控方法 ,是解决紫杉醇药源问题的最有前途的方法之一。     

Data fitting with a spline using a real-coded genetic algorithm

To obtain a good approximation for data fitting with a spline, frequently we have to deal with knots as variables. The problem to be solved then becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain the global optimum. In this paper, we propose a method for solving this problem by using a real-coded genetic algorithm. Our method can treat not only data with a smooth underlying function, but also data with an underlying function having discontinuous points and/or cusps. We search for the best model among candidate models by using the Bayes Information Criterion (BIC). With this, we can appropriately determine the number and locations of knots automatically and simultaneously. Five examples of data fitting are given to show the performance of our method.     

解Job－Shop调度问题的一个遗传算法

本文首先介绍了遗传算法的基本概念和流程，然后叙述了如何把Ｊｏｂ－Ｓｈｏｐ调度问题编码成为遗传算法的形式，并解释了对于实现这一算法中一些问题的考虑．最后给出了算法运行结果并对结果与算法做了总结。     

一种派生式与创成式相结合的CAPP系统

本文结合为某机械厂开发的实用计算机辅助工艺设计系统(JJCAPP),论述了一种兼有派生式和创成式特点的综合式工艺自动设计方法.     

大型水平输气管线优化设计新方法

提出了含进分气点的水平输气管线优化设计新方法，该方法对于传统的优化思想进行了改进，概念清晰，方法简便，同时，将遗传算法引入输气管线的优化设计中，与常规的优化方法相比，具有明显的优越性。     

新生牛肝活性肽的制备及其毒性检测

目的制备新生牛肝活性肽并评价其安全性。方法采用膜法分离制备新生牛肝活性肽。将3批制品于37～40℃,75%相对湿度条件下存放3个月,以多肽含量为指标观察其稳定性,并进行急性毒性试验、小鼠骨髓细胞微核试验、Ames试验、小鼠精子畸形试验和大鼠喂养试验。结果制备的3批新生牛肝活性肽存放3个月后,多肽含量无明显下降,质量稳定。小鼠和大鼠经口灌人大于20.0g/kg体重的新生牛肝活性肽,均无急性毒性。3种致突变试验均未显示出致突变性,大鼠喂养试验各项指标均未见明显毒性。结论新生牛肝活性肽未表现出明显毒性。     

分散图象压缩的遗传算法

基于分形和PIFS函数的特点，提出一种改进的遗传算法用于分形压缩分块的定义域匹配搜索算法。该方法克服了原来分形图象压缩中分类匹配算法固有的局部最优性和随机搜索的缺点，是一种具导引的全局优化的搜索算法。分析和实验表明，该方法的图象压缩比高，解码质量好，若应用遗传算法的并行性，其编码速度也得到提高。     

Surveillance test policy optimization through genetic algorithms using non-periodic intervention frequencies and considering seasonal constraints

In order to maximize systems average availability during a given period of time, it has recently been developed a non-periodic surveillance test optimization methodology based on genetic algorithms (GA). The fact of allowing non-periodic tests turns the solution space much more flexible and schedules can be better adjusted, providing gains in the overall system average availability, when compared to those obtained by an optimized periodic test scheme. This approach, however, turns the optimization problem more complex. Hence, the use of a powerful optimization technique, such as GA, is required.Considering that some particular features of certain systems can turn it advisable to introduce other specific constraints in the optimization problem, this work investigates the application of seasonal constraints for the set of the Emergency Diesel Generation of a typical four-loop pressurized water reactor in order to planning and optimizing its surveillance test policy. In this analysis, the growth of the blackout accident probability during summer, due to electrical power demand increases, was considered. Here, the used model penalizes surveillance test interventions when the blackout probability is higher.Results demonstrate the ability of the method in adapting the surveillance test policy to seasonal constraints. The knowledge acquired by the GA during the searching process has lead to test schedules that drastically minimize test interventions at periods of high blackout probability. It is compensated by more frequent redistributed tests through the periods of low blackout probability in order to improve on the overall average availability at the system level.