双交叉算子遗传算法在终端区飞机排序中的应用

当今，普遍的航班延误现象不仅增加了巨额飞行成本，还影响乘客体验. 对终端区待降飞机队列进行合理调整，可以提高跑道利用率，减少航班延误，达到降低延误代价的效果. 针对终端区飞机排序问题，提出一种包含双交叉算子的遗传算法，针对不同适应度染色体采取不同的交叉操作，使得在交叉过程中既能保护优质染色体，也能使其它染色体继续进化. 同时引入重排算子对变异后的子代进行优化，共同加快遗传算法收敛速度，使其更加符合实际使用需求. 实验结果表明，算法收敛速度得到改进，能在可接受时间内得到可行解.     

A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems

Genetic Algorithm (GA) has found wide application in path optimization problem. In many fields such as navigating system, oil transportation, paths between the starting node and the termination node often have distinct number of relay-nodes, which leads to the corresponding chromosomes would have different length. We refer to chromosomes with non-consistent lengths as the variable-length chromosomes. This paper first investigated GAs with variable-length chromosomes widely used and found that Same Point (SP) crossover is the most popular crossover mechanism. Then, a new crossover mechanism called Same Adjacency (SA) is proposed for GA with variable-length chromosomes for path optimization problem, which outperforms GA with SP by a better search capability as the mathematical analysis shows. The simulation study indicates that GAs with our crossover operators could obtain a better solution, as compared to GAs with SP, while still being able to converge fast in different networks with varied sizes.     

基于遗传算法的彩色图像滤波器研究

为实现彩色图像噪声的滤波,基于遗传算法技术构建了一种新型的彩色图像滤波器,在彩色图像的矢量中值滤波过程中,该滤波器利用遗传算法具有寻找全局最优解的能力,获得滤波器窗口的最优权值。从滤波效果看,有一定提高,并进一步提出了一种基于个体相似性的遗传算法：当种群的多样性较好时,采用标准的交叉策略;当种群的多样性较差时,根据个体的相似性选择个体配对,避免相同的个体配对交叉,减少高度相似个体进行配对交叉的概率,从而提高交叉操作的效率。     

求解有约束优化问题的实数遗传算法改进研究

提出一种求解有约束优化问题的改进实数遗传算法.首先,提出一种排序分组选择法,该方法具有种群多样性好、易于实现的优点.其次,给出一种基于方向的启发式交叉算子(DBHX),DBHX能够产生无数个交叉方向,且有可能产生一个引导参与交叉的染色体向最优解移动的方向vecD,即使交叉方向与vecD不一致,也有很大的可能性非常接近方向vecD,可以保证有很大的机会产生更好的子代染色体.最后,针对单一的变异算子无法兼顾局部搜索能力和全局搜索能力的缺点,提出一种组合变异方法,使得变异操作既能保证算法的局部搜索能力,又能兼顾全局搜索能力.10个实例的计算结果表明,所提出的改进实数遗传算法具有较快的收敛速度,从而验证了所提出算法的可行性和有效性.     

Linkage Discovery through Data Mining [Research Frontier]

Genetic algorithms (GAs) are extensively adopted in various aspects of data mining, e.g., association rules, clustering, and classification [1, 2, 3]. Instead of applying GAs for data mining, this study addresses linkage discovery, an essential topic in GAs, by using data mining methods. Inspired by natural evolution, GAs utilize selection, crossover, and mutation operations to evolve candidate solutions into global optima [4]. This evolutionary scheme can effectively resolve many search and optimization problems. As the most salient feature of GAs, crossover enables the recombination of good parts of two selected chromosomes, yet, in doing so, may disrupt the collected promising segments.     

多配送中心危险货物配送路径鲁棒优化

针对危险货物配送路径对不确定因素敏感度较高的问题,提出了鲁棒性可调的多配送中心危险货物配送路径鲁棒优化方法。首先,以最小化运输风险和最小化运输成本为目标,根据Bertsimas鲁棒离散优化理论,建立鲁棒优化模型;然后,在改进型强度Pareto进化算法（SPEA2）的基础上设计一种三段式编码的多目标遗传算法进行求解,在遗传操作中对不同染色体段分别采用不同的交叉和变异操作,有效避免了种群进化过程中不可行解的产生;最后,以庆阳市西峰区部分路网为例进行实证研究,并将配送方案落实到运输过程的路段中,形成具体的运输路径。研究结果表明：在多配送中心下,运用该鲁棒优化模型及算法,能快速得到具有较好鲁棒性的危险货物配送路径。     

一种基于复合交叉的实数编码遗传算法

提出了一种基于复合交叉的实数编码遗传算法。通过对父代染色体间的区域进行多次交叉操作,复合交叉操作保留了被搜索区域的信息。算法首先对父代染色体进行复合交叉操作,然后利用包含在新个体集合中的信息进行信息最大化选择,对每一代个体进行基于适应度的选择。集合中信息冗余的个体被从种群中删除,位于欠搜索区域中的个体被保留。由于算法能够始终保持种群的多样性,算法不仅能搜索到全局最优点,同时也能找到尽量多的局部极值点。利用算法对多极值函数进行了寻优,仿真结果表明了算法的有效性。     

二维最优进化图像分割算法

针对一维阈值分割只考虑图像的灰度级而不考虑像素的空间信息且需要目标函数的问题,根据最优进化图像阈值分割算法的基本思想,提出了一种无目标函数的二维图像阈值分割算法框架(2D-OEA)。2D-OEA将每个图像二维信息向量看作一个染色体,假设最优进化方向存在,建立进化方向更新模型;然后定义了染色体编码规则,通过简单随机采样初始化种群,再对种群进行交叉变异运算、适值计算、选择和阈值修正,得到稳定的最优二维阈值。分别从理论和实验分析了假设和模型的合理性。实验结果表明,假设和进化方向更新模型合理,2D-OEA快速、稳定且有效,分割结果优于OEA。     

基于镜像安装的正版Windows批量激活设计与实现

为了克服现有Windows的批量激活弊端,研究如何高效快速且可控的Windows批量激活方式,并进行了实验。     

成组技术中的P－中位模型的遗传算法

采用遗传算法对成组技术中的零件进行近优分蔟，提出了用０、１码分段构造染色体和分段交叉、变异的策略来满足Ｐ－中位模型复杂约束的要求。实验结果表明，当以类内样本距离之和作为评价准则时，该算法明显好于Ｋ－平均算法。     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

Forecasting of manufacturing cost in mobile phone products by case-based reasoning and artificial neural network models

The mobile phone manufacturers in Taiwan have made great efforts in proposing the rational quotations to the international phone companies with the ambition to win the bids by out beating other phone manufacturers. However, there are a lot of uncertainties and issues to be resolved in estimating the manufacturing costs for mobile phone manufacturers. As far as we know, there is no existing model which can be applied directly in forecasting the manufacturing costs. This research makes the first attempt to develop a hybrid system by integrated Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as a Product Unit Cost (PUC) forecasting model for Mobile Phone Company. According to the cost formula of the mobile phone and experts’ opinions, a set of qualitative and quantitative factors are analyzed and determined. Qualitative factors are applied in CBR to retrieve a similar case from the case bases for a new phone product and ANN is used to find the relationship between the quantitative factors and the predicted PUC. Finally, intensive experiments are conducted to test the effectiveness of six different forecasting models. The model proposed in this research is compared with the other five models and the MAPE value of the proposed model is the smallest. This research provides a new prediction model with high accuracy for mobile phone manufacturing companies.     

RGFGA: an efficient representation and crossover for grouping genetic algorithms

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree.We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.     

GD-SAT model and crossover line

In the last decade a lot of effort has been invested into both theoretical and experimental analysis of SAT phase transition. However, a deep theoretical understanding of this phenomenon is still lacking. It is still a very challenging problem to determine a relationship between crossover points for different SAT problems. This paper introduces one new class of randomly generated SAT problems, GD-SAT, and we experimentally show there is a phase transition for the problems in this class. On the basis of both analytical and experimental arguments we conjecture that there is a surprisingly simple, linear relationship between crossover points for problems in this class. This relationship is of both theoretical and practical importance.     

Regression models for estimating product life cycle cost

Product life cycle cost (LCC) is defined as the cost that is incurred in all stages of the life cycle of a product, including product creation, use and disposal. In recent years, LCC has become as crucial as product quality and functionality in deciding the success of a product in the market. In order to estimate LCC of new products, researchers have employed several (parametric) regression analysis models and artificial neural networks (ANN) on historical life cycle data with known costs. In this article, we conduct an empirical study on performance of five popular non-parametric regression models for estimating LCC under different simulated environments. These environments are set by varying the number of cost drivers (independent variables), the size of sample data, the noise degree of sample data, and the bias degree of sample data. Statistical analysis of the results recommend best LCC estimation models for variable environments in stages of the product life cycle. These findings are validated with real-world data from previous work.     

Coefficient estimation of IIR filter by a multiple crossover genetic algorithm

This paper proposes an improved genetic algorithm (GA) with multiple crossovers to estimate the system coefficients for the infinite impulse response (IIR) digital filter. In the traditional crossover operation, it needs two parent chromosomes to achieve the crossover work, whereas in this paper the proposed algorithm selects three chromosomes for crossover in order to generate more promising offspring toward the problem solution. Each of unknown IIR coefficients is called a gene and the collection of genes forms a chromosome. A population of chromosomes is evolved by the genetic operations of reproduction, multiple crossover, and mutation. Finally, two illustrative examples including the band pass and band stop IIR filters are demonstrated to verify the proposed method.     

Lot sizing with permissible delay in payments and entropy cost

Although the lot size problem (i.e., the economic order quantity model) has been widely accepted and used by researchers and practitioners, it has also been criticized by others on the grounds that not accounting for some of the hidden costs in inventory systems often lead to poor results. This paper postulates that estimating these hidden costs may be attained by applying the first and second laws of thermodynamics to reduce system entropy (or disorder) at a cost. The applicability of this concept is demonstrated for the economic lot size problem with permissible delay in payments. Mathematical models are developed with numerical examples provided and results discussed.     

Multiobjective topology optimization of structures using genetic algorithms with chromosome repairing

In this work, a genetic algorithm (GA) for multiobjective topology optimization of linear elastic structures is developed. Its purpose is to evolve an evenly distributed group of solutions to determine the optimum Pareto set for a given problem. The GA determines a set of solutions to be sorted by its domination properties and a filter is defined to retain the Pareto solutions. As an equality constraint on volume has to be enforced, all chromosomes used in the genetic GA must generate individuals with the same volume value; in the coding adopted, this means that they must preserve the same number of “ones” and, implicitly, the same number of “zeros” along the evolutionary process. It is thus necessary: (1) to define chromosomes satisfying this propriety and (2) to create corresponding crossover and mutation operators which preserve volume. Optimal solutions of each of the single-objective problems are introduced in the initial population to reduce computational effort and a repairing mechanism is developed to increase the number of admissible structures in the populations. Also, as the work of the external loads can be calculated independently for each individual, parallel processing was used in its evaluation. Numerical applications involving two and three objective functions in 2D and two objective functions in 3D are employed as tests for the computational model developed. Moreover, results obtained with and without chromosome repairing are compared.     

A hybrid approach of genetic algorithms and local optimizers in cell loading

In this paper, the potential application of genetic algorithms to cell loading is discussed. The objective is to minimize the number of tardy jobs. Three different approaches are proposed and later compared. The first approach consists of two steps where (1) genetic algorithms is used to generate a job sequence and (2) a classical scheduling rule is used to assign jobs to the cells. The second approach consists of three steps where steps 1 and 2 are identical to the first approach plus step (3) Local Optimizer is applied to each cell independently. The third approach is very similar to the second approach except that chromosomes are modified to reflect the changes due to learning with local optimizer. Experimentation results show that the number of cells and the crossover strategy adapted affect the number of tardy jobs found. The results also indicate that hybrid GA-local optimizer approach improves the solution quality drastically. However, it has been also shown that GA alone can duplicate the performance of the hybrid approach with increased population size and number of generations in some of the cases. Finally, the impact of learning on the solution quality was not as significant as expected.     

一种变形杂交算子——轮转杂交算子

杂交算子是遗传算法的三个基本算子之一,它在改进解群质量方面发挥着重要作用。现有遗传算法的杂交算子通常选择两个个体组成母本进行杂交运算。该文提出了一种选择三个个体组成母本进行轮转杂交的思想,文章称这种新的杂交算子为轮转杂交算子。首先通过理论分析证明,它能够扩大遗传算子搜索子空间,使收敛结果更趋于最优解。然后将其用于分布式系统任务调度问题,模拟结果说明是可行的。