遗传算法理论及其应用研究进展

首先阐述遗传算法的原理和求解问题的一般过程;然后讨论了近年来从遗传算子、控制参数等方面对遗传算法的改进,并对遗传算法在计算机科学与人工智能、自动控制以及组合优化等领域的应用进行陈述;最后评述了遗传算法未来的研究方向和主要研究内容。     

基于分裂算子的遗传算法和自适应遗传算法

针对遗传算法所存在的早熟和收敛速度慢等问题,基于低等生物的分裂生殖现象,提出了分裂算子的概念,并将该算子引入到传统遗传算法和自适应遗传算法中,对这两种遗传算法进行了改进。通过一系列多峰函数测试实验,将改进算法分别与基本遗传算法和自适应遗传算法进行比较,证明引入分裂算子后的遗传算法和自适应遗传算法不仅有效地收敛到全局最优解,而且提高了收敛速度。     

基于量子进化算法的路由选择

网络中存在许多设计和优化问题,其中相当一部分属于NP类型。传统的解法由于计算复杂度过大而失效。为了降低计算机网络的时延和运营费用以改进网络性能,采用量子进化算法优化计算机网络中路由选择问题,深入研究了量子进化算法及其在路由选择优化问题中的应用,并对量子进化算法进行了改进,使之更适合这类问题的求解。仿真实验结果表明,同传统优化算法相比该方法对求解网络的路由选择具有很大优越性。研究结果不仅对各类网络的优化问题有一定的应用价值,而且也扩展了量子进化算法的应用范围。     

一种基于遗传算法与进化编程的系统辨识方法

分析比较了遗传算法（GA）和进化编码（EP）在解决系统辨识问题中的优劣，提出一种将GA和EP相结合的新的系统辨识方法，该方法既不依赖于种群的初始值，又具有较强的稳定性。仿真结果表明了该方法的有效性和独到之处。     

遗传算法的粗糙集理论在文本降维上的应用

遗传算法作为一种有效的全局并行优化搜索工具,早被众多应用领域所接受。根据问题提出了相应的适应度函数,针对遗传算法和粗糙集理论两种方法各自的特点,将两种算法适当结合。还把结合后的方法和单一的粗糙集算法在文本分类效果上进行了对比。实验结果表明将遗传算法和粗糙集理论相结合的优化方法来应用到特征提取中,比单一的粗糙集算法,具有更好的降维效果,使得降维后的特征词更有利于文本数据的分类,大大优化了文本分类的效果。     

An Empirical Study of Multipopulation Genetic Programming

This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we discuss the role of the parameters that characterize the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.     

一种基于复数编码的遗传算法

第一次把复数编码的思想应用到遗传算法中去,用复数编码来表达双倍体,并具体规定了遗传操作.每一个复数对应于双倍体的一对等位基因.目标函数自变量的大小由其对应的复数的模决定,符号则由相应复数的幅角决定.与传统的实数编码的遗传算法相比,本算法大大地扩展了表达空间的维数,实验结果证明了本算法的有效性.     

硬件进化中演化算法的研究及应用

详细介绍了硬件进化的概念,硬件进化的原理与实现思想,遗传算法与蚁群算法动态融合的基本原理,融合后算法中遗传算法及蚁群算法规则.融合过程中遗传算法与蚁群算法动态衔接问题以及融合后的算法在硬件进化中的应用过程.最后,分析了通过该算法进化后硬件的进化应用前景.     

Tantrix: A Minute to Learn, 100 (Genetic Algorithm) Generations to Master

The game of Tantrix™ provides a challenging, mathematical and graphic domain for evolutionary computation. The simple task of forming long loops of colored arcs quickly becomes a search nightmare for humans and computers alike as the number of game pieces scales linearly. This paper introduces Tantrix-GA, a genetic algorithm that solves several types and sizes of Tantrix puzzles but still falls well short of (at least a few) human Tantrix experts. By introducing this problem to evolutionary computation researchers, we hope to motivate an evolutionary attack on the holy-grail Tantrix puzzles, one of which has yet to be solved by any intelligence, real or artificial.     

混合性能指标优化问题的大种群规模进化算法

混合性能指标优化问题可结合传统遗传算法和交互式遗传算法求解, 而种群规模和人机评价任务分配是影响算法性能的关键. 针对该问题, 本文提出一种新的进化优化算法. 首先, 采用大规模种群, 扩大搜索范围, 以增强算法的探索能力; 然后, 根据计算机和用户完成任务耗时的比值, 确定每代用户评价的个体数, 以提高计算机的使用效率; 接着, 采用K–均值聚类方法和基于相似度的估计策略, 以减轻用户疲劳; 最后, 采用Pareto占优比较不同个体的优劣, 使得最优解有较好的显式性能指标值和隐式性能指标值. 将本文算法应用于室内布局这一混合性能指标优化问题, 结果验证了所提算法的有效性.     

A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization

This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749-768; T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, Multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291-296; K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON’2004, Busan, 2004, pp. 1268-1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization.     

Supply Estimation Using Coevolutionary Genetic Algorithms in the Spanish Electrical Market

The price of electrical energy in Spain has not been regulated by the government since 1998, but determined by the supply from the generators in a competitive market, the so-called electrical pool. A genetic method for analyzing data from this new market is presented in this paper. The eventual objective is to determine the individual supply curves of the competitive agents. Adopting the point of view of the game theory, different genetic algorithm configurations using coevolutionary and non-coevolutionary strategies combined with scalar and multi-objective fitness are compared. The results obtained are the first step toward solving the induction of the optimal individual strategies into the Spanish electrical market from data in terms of perfect oligopolistic behavior.     

遗传算法在软件项目管理中的应用及研究

遗传算法（genetic algorithms,GAs）因其能适应任意限制条件和目标问题,被普遍应用在各种调度优化问题中,但是针对于特定的软件项目管理问题和环境,没有系统的研究和分析.通过对传统调度问题中遗传算法的研究,结合软件项目管理的特点,提出和比较了基于任务和基于时间轴的两种模型,以及GA编码和算子的设计.并通过与其他启发式算法上的性能比较实验,确认了GA在软件项目管理问题中的优势.     

一种改进选择算子的遗传算法

遗传算法（Genetic Algorithm,GA）是一种模拟生物进化的智能算法,被广泛应用于求解各类问题。简单遗传算法（Simple GA）仅靠变异产生新的数值,常常存在搜索精确度不高的问题。针对这个问题,对SGA的选择算子进行改进,即把相似个体分在同一组中,以组为单位进行选择,并通过该组个体的特点进行高斯搜索生成新的群体。这样使得GA在搜索过程中不仅可以很好地保持个体的多样性,并且可以提高解的精确度。通过对11个函数（单峰和多峰）的仿真实验,证明了采用新的选择算子后,GA在求解问题的精确度上有了很大地改善。     

基于模糊控制及遗传算法的反向选择算法

针对基于人工免疫的入侵检测技术中所使用的传统反向选择算法,在面对大量的网络通信数据或具有多个分离特征区间网络通信数据时的无效性,提出了基于模糊控制及遗传算法的反向选择算法.在利用反向选择算法生成抗体时,首先利用模糊控制原理来确定抗体的数量,使得计算机中抗体的数量处于最优,然后为了达到在一定数量抗体时种群的总体免疫力最大,引入了遗传算法来进化种群,最终使得在计算机中抗体的数量得到控制,同时在该数量下种群具有最大的免疫力.     

Genetic programming: principles and applications

Genetic algorithms (GA) has given rise to two new fields of research where (global) optimisation is of crucial importance: ‘genetic based machine learning’ (GBML) and ‘genetic programming’ (GP). An introduction by the authors to GA and GBML was given in two previous papers (Eng. Appl. Artif. Intell. 9(6) (1996) 681; Eng. Appl. Artif. Intell. 13(4) (2000) 381). In this paper, the last domain (GP) will be introduced, thereby making up a trilogy which gives a general overview of the whole field. In this third part, an overview will be given of the basic concepts of GP as defined by Koza. A first (educational) example of GP is given by solving a simple symbolic regression of a sinus function. Finally, a more complex application is presented in which GP is used to construct the mathematical equations for an industrial process. To this end, the case study ‘fibre-to-yarn production process’ is introduced. The goal of this study is the automatic development of mathematical equations for the prediction of spinnability and (possible) resulting yarn strength. It is shown that (relatively) simple equations can be obtained which describe accurately 90% of the fibre-to-yarn database.     

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.     

Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance

The development of powerful computers and faster input/output devices coupled with the need for storing and analyzing data have resulted in massive databases (of the order of terabytes). Such volumes of data clearly overwhelm more traditional data analysis methods. A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. In this paper we present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of self-adaptive feature selection together with a wrapper feature selection method based on Hausdorff distance measure.