基于遗传算法的图论聚类方法

针对传统图论聚类算法对初始聚类中心的敏感性以及聚类结果与样本输入次序等问题,提出了基于遗传算法进行图论聚类分析的基本原理和实现方法.实验结果表明,遗传算法应用于图论聚类分析能够搜索到更为精确的聚类中心值,其结果明显好于传统图论聚类算法.     

Multi-objective optimisation of lidar parameters for forest-fire detection on the basis of a genetic algorithm

A numerical model for the estimation of the laser pulse energy required for forest-fire detection with a predefined signal-to-noise ratio by an eyesafe lidar operating at wavelength of 1.54 μm is developed. This model is coupled with a genetic algorithm, which is used for multi-objective optimisation of the fitness function (weighted sum of laser price, telescope price, and surveillance time) using telescope diameter and laser beam divergence as independent variables.     

Global and Local Search Algorithms for Concave Cost Transshipment Problems

Traditionally, the minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Recently, some advanced local search algorithms have been developed that can directly solve concave cost bipartite network problems. However, they are not applicable to general transshipment problems. Moreover, the effectiveness of these modified local search algorithms for solving general concave cost transshipment problems is doubtful. In this research, we propose a global search algorithm for solving concave cost transshipment problems. Effecient methods for encoding, generating initial populations, selection, crossover and mutation are proposed, according to the problem characteristics. To evaluate the effectiveness of the proposed global search algorithm, four advanced local search algorithms based on the threshold accepting algorithm, the great deluge algorithm, and the tabu search algorithm, are also developed and are used for comparison purpose. To assist with the comparison of the proposed algorithms, a randomized network generator is designed to produce test problems. All the tests are performed on a personal computer. The results indicate that the proposed global search algorithm is more effective than the four advanced local algorithms, for solving concave cost transshipment problems.     

遗传算法的自适应进化策略及TSP问题的遗传优化

本文研究遗传算法的进化策略，提出了一种在遗传操作层次上将局部搜索方法与基本遗传算法要结合，依据遗传群体的环境参量动态地调整遗传算法的进化策略和控制局部搜索强度的自适应进化策略，并结合货郎担（ＴＳＰ）优化问题介绍其具体实现方法，给出实验结果。     

Analysis of crossovers and selections in a coarse-grained parallel genetic algorithm

The parallel genetic algorithms (PGA) have been developed for combinatorial optimization problems, and its parallel efficiencies have been investigated on a specific problem. These investigations were concerned with how to design a topology and the determination of the optimum setting for parameters (for example, size of subpopulations, migration interval, and so on) rather than the effectiveness of genetic operators. This paper investigates a relation between the parallel efficiency of the coarse-grained PGA and genetic (crossover and selection) operators for the traveling salesman problem on an MIMD parallel computer. The following genetic operators are considered: improved edge recombination (IERX), distance preserving (DPX), and complete subtour exchange (CSEX) crossovers, and two selection operators, which have relatively high selection pressures. Computational results indicate that the parallel efficiency is significantly affected by the difference of crossovers rather than the selections, and the PGA with CSEX gives better properties.     

基于B样条曲线及遗传算法求解TA/TA问题

主要讨论了水平面上空面导弹TA／TA（地形回避／威胁回避）问题。首先通过Dijkstra算法求出地形、威胁包络的最短及几个较短路径通道；用所求通道上的关键点作为三次B－样条曲线的顶点，近似拟合导弹飞行路线。以本文提出的最小距离法生成适应度函数，考虑导弹机动能力的约束，通过遗传算法来调整B－样条曲线的顶点，从而寻找出性能指标最优的可飞行样条曲线；根据所求出的最优样条曲线给出导弹航路点。通过大量的仿真实验，证明该方法是可行的，具有快速性并适应于大范围、复杂地形和威胁环境。     

THE FEATURE SUBSET SELECTION ALGORITHM

The motivation of data mining is how to extract effective information from huge data in very large database.However,some redundant irrelevant attributes,which result in low performance and high computing complexity,are included in the very large database in general.So,Feature Selection(FSS)becomes one important issue in the field of data mining.In this letter,an Fss model based on the filter approach is built,which uses the simulated annealing gentic algorithm.Experimental results show that convergence and stability of this algorithm are adequately achieved.     

基于遗传算法的重叠多播路由计算

针对目前网络中的多播技术，文章介绍了解决重叠多播路由问题的一些模型，以其中的一些作为参考，结合其它算法的优点，提出了同时兼顾网络延迟与负载平衡的新模型。并提出以Pruefer序列作为染色体编码为基础的遗传算法来求解这个模型。对算法的复杂度进行分析，最后通过仿真试验表明了算法的可行性与有效性。     

A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability

A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-means + HLA). Our proposed method has the best generalization performance.