低能电子衍射表面结构分析中引入全局优化算法——遗传算法的研究

将遗传算法(GAs)应用到低能电子衍射(LEED)表面结构分析方法中,编制了Gas-LEED结构自动搜寻的计算程序,并以Pt(111)-p(2×2)O吸附系统为例,获得了具有全局优化特点的结构参数。     

反向电渗流非水毛细管电泳法快速测定微量赤霉素

赤霉素是一类重要的植物激素,是结构相似的弱酸性二萜类化合物。针对毛细管电泳法分离、测定赤霉素速度慢、效率不佳等问题,该文发展了一种可快速测定微量活性赤霉素的非水毛细管电泳-紫外检测法。以聚环氧乙烷动态涂布毛细管,利用正离子使电渗流反向,将含10 mmol/L醋酸铵的甲醇-水(95∶5,v/v)作为缓冲体系,在0.08%(v/v)醋酸含量下,分离8种内源性赤霉素。结果表明,赤霉素出峰时间的相对标准偏差(RSD)≤2.1%(日内)或≤4.3%(日间),峰面积的RSD≤4.5%(日内)或≤6.9%(日间),检出限(S/N=3)为1.04~2.20 mg/L,相关系数为0.998 2~0.999 3,回收率为87.2%~93.5%。该方法简单、快速、稳定,以含醋酸铵的甲醇水溶液为缓冲体系,预先考虑了质谱测定的需要,可用于实际样品如麦芽中赤霉素的分析,具有一定的应用价值。     

遗传算法在EXAFS谱图解析中的应用

扩展Ｘ射线吸收精细结构（ＥＸＡＦＳ）谱是研究物质原子近邻结构和表面结构的有力工具。ＥＸＡＦＳ谱的解析通常采用标准样品比较法或最小二乘曲线拟合方法。但前者对标样的要求很高，而后者则参数初值难以确定，且结果有时不唯一。本文提出一种ＥＸＡＦＳ曲线拟合的新方法一遗传算法，并对单配位层Ｃｕ样品的ＥＸＡＦＳ谱图进行了解析，取得满意的结果。     

Optimization of an Active Noise Control System Inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm

In the last decade, Active Noise Control (ANC) has become a very popular technique for controlling low-frequency noise. The increase in its popularity is a consequence of the rapid development in the fields of computers in general, and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. Even though the physical background of the ANC systems is well known and understood, efficient tools for the optimization of the sensor and actuator configurations of the ANC system, based on classical optimization methods, do not exist. This is due to the nature of the problem that allows the calculation of the effect of the ANC system only when the sensor and actuator configurations are specified. An additional difficulty in this problem is that the sensor and the actuator configurations cannot be optimized independently, since the effect of the ANC system directly depends on the combined sensor and actuator configuration. For the solution of this problem several other optimization techniques were applied, such as simulated annealing for example. In this paper the successful application of a Genetic Algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented. The results obtained from the application of the GA are very promising. The GA was able to identify various configurations that achieved a reduction of 6.3 dBs to 6.5 dBs, which corresponds to an actual reduction of 50% of the initial acoustic pressure.     

Combining evolutionary and stochastic gradient techniques for system identification

In the present contribution, a novel method combining evolutionary and stochastic gradient techniques for system identification is presented. The method attempts to solve the AutoRegressive Moving Average (ARMA) system identification problem using a hybrid evolutionary algorithm which combines Genetic Algorithms (GAs) and the Least Mean Squares LMS algorithm. More precisely, LMS is used in the step of the evaluation of the fitness function in order to enhance the chromosomes produced by the GA. Experimental results demonstrate that the proposed method manages to identify unknown systems, even in cases with high additive noise. Furthermore, it is observed that, in most cases, the proposed method finds the correct order of the unknown system without using a lot of a priori information, compared to other system identification methods presented in the literature. So, the proposed hybrid evolutionary algorithm builds models that not only have small MSE, but also are very similar to the real systems. Except for that, all models derived from the proposed algorithm are stable.     

AN EFFICIENT GENETIC ALGORITHM FOR LARGE SCALE BUILT-UP STRUCTURAL OPTIMIZATION

In this paper,a new zigzag method for plate structures and a geneticalgorithm (GA) of dynamic source seed spaces are developed and a combination ofthem is used to deal with large scale built-up structural optimization.The new GAcombined with the zigzag method can work efficiently when coping with large scalestructural optimization included displacement and stress constraints.Examples showthat this GA is robust and can be used for many complex structural optimizationproblems.     

Optimal Gabor Kernel's Scale and orientation selection for face classification

2D Gabor-based face representation has attracted much attention. However, owing to the fact that Gabor features are redundant and too high-dimensional, appropriate feature dimension reduction appears to be much more paramount. Allowing for each individual Gabor feature constructed by a combination of scale and orientation pair, we equate feature dimension reduction problem to optimal Gabor kernels’ scales and orientation selection problem. Genetic algorithms (GAs) have represented a useful tool for optimal subset selection. However, population premature and optimization stagnancy problems exist in traditional GAs. Here we present an improved algorithm: Hybrid Genetic algorithms-based (HGAsb), which introduces the concept of the simulated annealing into traditional GAs to effectively solve the problems mentioned above and to improve optimization efficiency. Experimental results on IMM face database demonstrate that in contrast to GAs, our proposed algorithm can provide 4.25 improvements. The distributions of orientations and scales of the selected features by HGAsb are also analyzed. Results indicate that the features in the larger scales have equal importance as those in the smaller scales in discriminating nuance of faces. The features in horizontal, vertical and 225° orientations have more discriminative power.