连续型遗传算法和遗传规划在投影变换中的应用初探

回顾了地图投影变换的基本方法,阐述连续型遗传算法和遗传规划的原理和方法,探讨这两优化方法在地图投影变换中的应用.     

基于GA的字符串型测试数据自动生成方法研究

实现了一种基于GA的字符串型测试数据自动生成方法．该方法设计采用了混合适应度函数,并且自动生成的测试数据是在实际应用中使用相当普遍但目前测试数据自动生成方法研究中涉及较少的字符串型数据．实验表明：该字符串型测试数据的自动生成方法有较好的效果．它可以有效地帮助做毕业系统设计的计算机专业学生和软件开发人员自动生成字符串类型的测试数据,提高软件测试的效率．     

基于GA-BP网络的铁水硅质量分数预测系统

提出将BP神经网络与遗传算法(GA)相结合,建立了三层GA-BP神经网络模型,模型利用遗传算法来修改网络的连接权值,构筑进化型的神经网络模型,缩短网络学习训练时间,提高模型预测精度。炉温预测主要是高炉铁水硅质量分数的预测,当要求硅质量分数预报的绝对误差为±0.05%时,命中率为90%。结果表明,GA-BP网络模型比传统的BP网络模型能够获得更高的精度。     

Optimal design of pressure vessel using an improved genetic algorithm

As the idea of simulated annealing （SA） is introduced into the fitness function, an improved genetic algorithm （GA） is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure constraint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis （FEA）. A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.     

基于遗传算法的染色体医学图像处理研究

生物医学图像的数字化处理是医学领域中的一个重要的发展方向。其中染色体的自动分析是细胞遗传学研究中急需解决的问题,文中针对自动分析中的前期图像处理提出了基于遗传算法的染色体图像分割方法,该方法充分考虑了待分割染色体的数量及人类显带染色体被明暗交迭的带纹分割为若干不连续的片段等特点。从实验结果可以看出,此方法具有分割速度快、鲁棒性好等优点。     

Analyzing the Interdependence Between Some Certain Gene Loci by Epistasis Measures in Fitness Landscapes of Schemata

GA-hardness and interdependence between genes in the chromosome are important questions in the study of genetic algorithms (GA) ,Traditional methods,which are used to measure the interaction between genes,can only reflect the extent of epistasis between all genes in the chromosome, Therefore,the definition of the fitness landscape of schemata is proosed in this paper,and epistasis measures on this landscape of schemata are used to analyze the degree of interdependence between some certain gene loci in study ,Some information between these sites can be reflected by some characters of the fitness landscape of schemata which are composed of these fixed sites,The stronger the interaction between these sites,the larger the variation of the fitness of schemata whose fixed sites correspond to those sites in study ,and the more rugged the fitness landscape of these schemata,According to the degree of interaction between given gene loci ,building blocks of GA can be analyzed and determined,and further genetic operators and the structure of GA can be designed and adjusted to improve the perfomance of GA ,At last,a lot of experiments including NK-models are done,and results of empirical analysis show that this method is effective.     

向日葵种子根的向光反应特性及其影响因素

研究了向日葵种子根的向光反应特性及光照时间、光照强度IAA和GA3对其向光性的影响。结果表明:(1)向日葵种子根表现为负向光性倾斜生长;(2)在2~8h的光照时间内,向日葵种子根的负向光性与光照时间成正比;(3)在5~25 lx的光强范围内,向日葵种子根的负向光性与光强成正比;(4)0~10-4mol.L-1的IAA可促进根的伸长和增强其负向光性反应,随着IAA浓度的升高,向日葵种子根的负向光性反应消失,生长也受到抑制;(5)10-5~10-4 mol.L-1的GA3能促进向日葵种子根的伸长,但对其负向光性无明显影响。     

Algorithm for 2D irregular-shaped nesting problem based on the NFP algorithm and lowest-gravity-center princlple

The nesting problem involves arranging pieces on a plate to maximize use of material. A new scheme for 2D irregular-shaped nesting problem is proposed. The new scheme is based on the NFP （No Fit Polygon） algorithm and a new placement principle for pieces. The novel placement principle is to place a piece to the position with lowest gravity center based on NFP. In addition, genetic algorithm （GA） is adopted to find an efficient nesting sequence. The proposed scheme can deal with pieces with arbitrary rotation and containing region with holes, and achieves competitive results in experiment on benchmark datasets.     

并行分布式遗传算法的研究

传统遗传算法在面对一些搜索空间巨大的复杂问题时,其表现往往难以令人满意。作者针对传统遗传算法解决高维多峰值问题时可能会出现的困难进行了分析,然后根据困难出现的原因,基于PVM设计了并行分布式遗传算法,并对适应度评估、交叉、变异算子做了一些改进,旨在加强算法的全局搜索能力,提高算法的收敛速度。为了验证算法多项措施的有效性,对一多峰函数在高维条件下进行多方面的测试,实验结果表明这几项措施是有效的。     

Optimization of membership function for fuzzy control based on genetic algorithm and its applications

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