Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques

Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.     

Using Genetic Algorithms for Concept Learning

In this article, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continually learns and refines concept classification rules from its interaction with the environment. The use of GAs is motivated by recent studies showing the effects of various forms of bias built into different concept learning systems, resulting in systems that perform well on certain concept classes (generally, those well matched to the biases) and poorly on others. By incorporating a GA as the underlying adaptive search mechanism, we are able to construct a concept learning system that has a simple, unified architecture with several important features. First, the system is surprisingly robust even with minimal bias. Second, the system can be easily extended to incorporate traditional forms of bias found in other concept learning systems. Finally, the architecture of the system encourages explicit representation of such biases and, as a result, provides for an important additional feature: the ability todynamically adjust system bias. The viability of this approach is illustrated by comparing the performance of GABIL with that of four other more traditional concept learners (AQ14, C4.5, ID5R, and IACL) on a variety of target concepts. We conclude with some observations about the merits of this approach and about possible extensions.     

Combining the Filtered Evaluation Function With Coevolutionary Shared Niching in Genetic Algorithms

In this paper, we propose the combination of filtered evaluation and coevolutionary shared niching (CSN) for extending the search ability of genetic algorithms (GA). The proposed scheme can overcome the problems of the filtering GA (FGA) and the CSN. The successful optimization ability of the FGA is supported by the filtered evaluation method that can modify the landscape for escaping local optima. However, the problem of the FGA is the relatively high cost to maintain the filter. The CSN can autonomously maintain the shared distance using the coevolution between two populations (called customers and businessmen). However, the escaping ability from local optima of the CSN is still insufficient. Therefore, the combination of the filtered evaluation and the CSN is proposed, to reduce the cost of the FGA filter. The effectiveness of the proposed scheme is confirmed through test problems.     

遗传算法在逃逸机动策略中的应用研究

分析了基于强化学习原理和遗传算法的序贯决策规则的自动学习方法,从规划报偿和规则激活度的角度讨论和研究了规则的信度分配问题,解决了在大的状态空间中搜索和延迟评价问题,为处理复杂的决策过程提供了一种行之有效的方法。基于该方法实现了飞机的逃逸机动策略,仿真结果表明了该方法的有效性。     

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。     

应用逻辑操作改善遗传算法性能

从理论上分析了传统变异算子存在的不足,借鉴数字技术的有关原理为基因增添了逻辑表达能力,引入逻辑运算规则改进的了传统的遗传算法 子,实验结果表明,改进型遗传算法收敛速度快,克服早熟能力强。     

基于遗传算法的非线性系统模型参数估计

针对一性系统系统模型的多样性,提出适用于多种非线性模型的基于遗传算法的参数估计方法,并以多种非线性模型为例作了仿真研究,结果表明,遗传算法是非线性系统模型参数估计的有效工具。     

基于遗传算法的非线性系统模型参数估计

针对非线性系统模型的多样性 ,提出适用于多种非线性模型的基于遗传算法的参数估计方法 ,并以多种非线性模型为例作了仿真研究 .结果表明 ,遗传算法是非线性系统模型参数估计的有效工具     

应用遗传算法辨识Hammerstein模型

基于遗传算法,提出了一种辨识Hammerstein模型的方法,该方法能够克服有色观测噪声的污染,获得非线性静态环节参数和线性动态环节参数的无偏估计,并与Hammerstein模型的MSLS辨识方法进行了比较,仿真结果说明了该方法的有效性。     

改进遗传算法在非线性变参数估计中的应用

针对解决非线性系统模型变参数估计问题要求运算速度快、效率高的情况，提出了实数编码遗传算法的改进算法，即分阶段设置收敛判断 条件及择优操作等操作步骤。分阶段设置收敛判断条件是指在交叉操作完成之后增加了判断是否收敛的操作步骤；择优操作是指用交叉操作后形成的解群中的适应值最大的优秀个体替代适应值最小的最差个体，使参加变异的优秀个体数目增加，使得算法在优秀个体局部邻域内的搜索机会增加。通过实例对改进算法与原算法的运算性能进行了比较，收敛速度平均提高了2-3倍。实验结果表明，改进算法对提高遗传算法的运算速度是可行和有效的。     

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.     

基于遗传算法的变压供水控制研究

变压供水已越来越广泛应用于生活领域,针对供水系统非线性、大滞后、参数时变的特点,本文提出了基于遗传算法的变压供水控制的方案,并在系统中应用改进型Smith预估器。通过仿真分析表明,系统具有良好的自适应性,可以在较大程度上保证最不利点的水压的快速调节和稳定,具有较高的应用价值。     

基于自适应遗传算法的控制器自动化设计

基于改进的自适应遗传算法，实现了基于性能分析的自动化优化设计控制器参数的目的。采用的遗传算子包括：二进制多参数级联编码方法；适应度函数的构造综合考虑误差和误差的变化量；选择操作采用比例算子与精英保存策略相结合；两点交又和多点变异，且交叉和变异概率均采用自适应策略。仿真结果表明了自适应遗传优化用于控制系统设计的有效性。     

应用遗传算法原理确定函数的最优解

本文主要介绍了一种新型的、随机性的全局优化方法即遗传算法.一般应用于在一个问题的解集中查找最优解情况,如是一个问题有多个答案,但是想查找一个最优答案的话,那么使用遗传算法可以达到更快更好的效果.即在浮点编码遗传算法中加入一个函数,构成适于不可微函数全局优化的遗传算法.该算法改善了遗传算法的局部搜索能力,显著提高了遗传算法求得全局解的概率.     

Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs

We study a general equilibrium system where agents have heterogeneous beliefs concerning realizations of possible outcomes. The actual outcomes feed back into beliefs thus creating a complicated nonlinear system. Beliefs are updated via a genetic algorithm learning process which we interpret as representing communication among agents in the economy. We are able to illustrate a simple principle: genetic algorithms can be implemented so that they represent pure learning effects (i.e., beliefs updating based on realizations of endogenous variables in an environment with heterogeneous beliefs). Agents optimally solve their maximization problem at each date given their beliefs at each date. We report the results of a set of computational experiments in which we find that our population of artificial adaptive agents is usually able to coordinate their beliefs so as to achieve the Pareto superior rational expectations equilibrium of the model.     

遗传算法在井口定位问题的应用研究

采用遗传算法对丛式井组井口定位问题进行了研究．先推导了较有价值的求解本问题最优解的算法，包括目前采用的经验法和基本的穷举法。然后应用遗传算法求解个目标靶井情况下到各顶点距离最短的最优解，结果具有较好的收敛性．     

Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms

An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduler operates in an environment with dynamically changing resources and adapts to variable system resources. It operates in a batch fashion and utilises a genetic algorithm to minimise the total execution time. We have compared our scheduler to six other schedulers, three batch-mode and three immediate-mode schedulers. Experiments show that the algorithm outperforms each of the others and can achieve near optimal efficiency, with up to 100,000 tasks being scheduled     

遗传算法的性能分析研究

回顾了遗传算法的理论研究状况,介绍了NoFreeLunch定理,描述了遗传算法的通用框架,构造了遗传算法的性能分析矩阵,并通过模拟实验分析了一系列遗传算法的性能.实验表明,这种评价算法性能的方法切实可行,可操作性好,具有一定的通用性.     

遗传算法在Snake模型中的应用

文章针对Snake模型应用于图像边缘检测时对于噪音过于敏感的不足,提出了一种改进的方法。利用遗传算法的全局优化特性改进Snake模型局部优化的缺点,并取得了较好的效果。