Model Selection Using Information Criteria and Genetic Algorithms

Automated model searches using information criteria are used for the estimation of linear single equation models. Genetic algorithms are described and used for this purpose. These algorithms are shown to be a practical method for model selection when the number of sub-models are very large. Several examples are presented including tests for bivariate Granger causality and seasonal unit roots. Automated selection of an autoregressive distributed lag model for the consumption function in the US is also undertaken.JEL classifications: C32, C69     

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.     

基于遗传算法的特征选择方法

特征提取广泛应用于模式识别、知识发现、机器学习等诸多领域,并受到了越来越多的关注犤1犦。对于一个给定的待分类模式,特征提取要求人们从大量的特征中选取一个最优特征子集,以代表被分类的模式。该文对特征提取这一组合优化及多目标优化问题提出了基于遗传算法的解决方法,把遗传算法作为识别或分类系统的“前端”,找出代表问题空间的最优特征子集,以大大降低分类系统的搜索空间,从而提高搜索效率。     

用GA寻优线性系统模糊控制器规则

控制精度和自适应能力一直是模糊控制应用中较难解决的问题,解决这一问题的关键在于选取适当的控制规则,而遗传算法可以较好地解决常规的数学优化技术所不能有效解决的问题。该文给出了对于具有修正因子的控制规则,采用遗传算法对其参数进行自调整的方法,它可提高模糊控制器的性能。通过仿真实验表明了该方法对于线性系统的控制是有效的。     

遗传算法自适应模糊神经网络控制

神经网络能够以任意精度逼近任意复杂的非线性关系,具有高度的自适应和自组织性,在解决高度非线性和严重不确定系统的控制方面具有巨大的潜力.但一般神经网络训练算法如BP算法训练速度慢,受初值影响大且易陷入局部极小点,该文提出了一种基于模糊神经网络的间接自校正控制系统,控制器以高斯隶属度函数的径向基函数（RBF）神经网络结构,利用改进的遗传算法（GA）对结构和参数进行同步优化,改进适应度函数指导搜索过程,在保证稳定情况下大大加快了收敛的速度.神经网络正向模型（NNP）利用弹性BP算法进行离线辨识,使得到的模型泛化性能好.     

Item-Location Assignment Using Fuzzy Logic Guided Genetic Algorithms

In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the item-location assignment problem, faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the required sum of the total traveling time of the workers to complete all orders is minimized. A stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) is proposed to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. The performance of this novel crossover operation is tested and is shown to be more effective by comparing it to other crossover methods. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make comparisons with the FLGA through simulations, various search methods such as branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, tabu search, differential evolution, and two modified versions of differential evolution are adopted. Results show that the FLGA outperforms the other search methods in all of the three considered scenarios.      

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。     

Using Genetic Algorithms to Model Road Networks

A model based on genetic algorithms views the allocation of people and private vehicles as an optimization problem, anticipating traffic congestion effects and adjusting the infrastructure accordingly.     

飞行器有效载荷运控模式的优化设计

提出一种通过遗传算法来对飞行器有效载荷运控模式进行自动控制的设计方案。设计考虑到遗传算法能够克服传统的优化方法的局限性,适合于大规模多区域的搜索,并有很好的鲁棒性,应用到有效载荷运控模式自动控制的设计中,有效地改善以往运控模式费时费力的状况。设计中改进了传统的遗传算法,将遗传个体设计成矩阵形式,有利于准确地描述载荷的工作状态。计算机仿真结果表明,该设计完全能满足对飞行器有效载荷进行控制的技术要求。     

面向入侵检测的基于多目标遗传算法的特征选择

针对刻画网络行为的特征集中存在着不相关或冗余特征,从而导致入侵检测性能下降的问题,本文提出了一种基于多目标遗传算法的特征选择方法,将入侵检测中的特征选择问题视为多目标优化问题来处理。实验结果表明,该方法能够实现检测精度与检测算法复杂性的均衡优化,在显著提高检测算法效率的同时,检测精度也有所提高。     

Using Disruptive Selection to Maintain Diversity in Genetic Algorithms

Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. With their great robustness, genetic algorithms have proven to be a promising technique for many optimization, design, control, and machine learning applications. A novel selection method, disruptive selection, has been proposed. This method adopts a nonmonotonic fitness function that is quite different from conventional monotonic fitness functions. Unlike conventional selection methods, this method favors both superior and inferior individuals. Since genetic algorithms allocate exponentially increasing numbers of trials to the observed better parts of the search space, it is difficult to maintain diversity in genetic algorithms. We show that Disruptive Genetic Algorithms (DGAs) effectively alleviate this problem by first demonstrating that DGAs can be used to solve a nonstationary search problem, where the goal is to track time-varying optima. Conventional Genetic Algorithms (CGAs) using proportional selection fare poorly on nonstationary search problems because of their lack of population diversity after convergence. Experimental results show that DGAs immediately track the optimum after the change of environment. We then describe a spike function that causes CGAs to miss the optimum. Experimental results show that DGAs outperform CGAs in resolving a spike function.     

Feature Selection Using the Domain Relationship with Genetic Algorithms

Considering the importance of the domain relationship in eliminating noisy features in feature selection, we present an alternate approach to designing a multi-objective fitness function using multiple correlation for the genetic algorithm (GA), which is used as a search tool in the problem. Multiple correlation is a simple statistical technique that uses the multiple correlation coefficients to measure the relationship between a dependent variable and a set of independent variables within the domain space. Simulation studies were conducted on both real-world and controlled data sets to assess the performance of the proposed fitness function. The comparison between the traditional fitness function and our proposed function is also reported. The results show that the proposed fitness function can perform more satisfactorily than the traditional one in all cases considered, including different data types, multi-class and multi-dimensional data.     

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     

Heterogeneous Computing and Parallel Genetic Algorithms

This paper analyzes some technical and practical issues concerning the heterogeneous execution of parallel genetic algorithms (PGAs). In order to cope with a plethora of different operating systems, security restrictions, and other problems associated to multi-platform execution, we use Java to implement a distributed PGA model. The distributed PGA runs at the same time on different machines linked by different kinds of communication networks. This algorithm benefits from the computational resources offered by modern LANs and by Internet, therefore allowing researchers to solve more difficult problems by using a large set of available machines. We analyze the way in which such heterogeneous systems affect the genetic search for two problems. Our conclusion is that super-linear performance can be achieved not only in homogeneous but also in heterogeneous clusters of machines. In addition, we study some special features of the running platforms for PGAs, and basically find out that heterogeneous computing can be as efficient or even more efficient than homogeneous computing for parallel heuristics.     

基于遗传算法的点群目标选取模型

结合 3种点群目标选取的一般原则和遗传算法的基本原理与特点 ,设计了基于遗传算法的点群目标选取模型 .考虑到要最大限度地保持点群的分布范围、排列规律、内部各地段的分布密度等因素 ,基于遗传算法的点群选取模型的基本原理是 :首先采用自适应分类方法 ,将点群 M依照密度分成若干类子点群 ,然后根据每个子点群的点数和最后要保留的总的点数 ,计算每个子点群中要保留的点数 ,最后结合凸壳化简方法和遗传算法对点进行选择 .在对关键性步骤进行讨论的基础上 ,本文针对某一地区的点群目标分别采用基于遗传算法的点目标选取方法与凸壳选取方法进行了选取对比实验 .从实验结果和遗传算法的特点分析可以看出 ,基于遗传算法的点目标选取方法的特点是非常明显的 ,其适用于分散式居民地记号房、可看作点状目标的小湖泊群等点状要素的选取 ;能够保持密度分布特征及其排列规律 ;外围轮廓特点没有大的改变     

基于遗传算法的模糊决策树的参数优化

模糊决策树归纳学习是从示例中产生规则知识的一个重要方法,决策树的产生过程涉及到两个重要的参数α、β。一般说来,这两个参数的选取依赖于所讨论的领域知识和用户的需要,若选取不当,会对分类结果产生很大影响,从而导致不正确的分类。如何选取这两个参数的值目前尚无较好的方法,仅凭人们的经验而定,该文提出了一种应用遗传算法来优化模糊决策树中参数的方法,旨在为选取参数提供实验方法,同时也为直接选取经验参数提供了一定的实验支撑。     

基于遗传算法的模糊控制器的综合优化设计

该文简要叙述了模糊控制器的优化设计原理,提出了一种采用遗传算法对模糊控制器的量化因子、比例因子、隶属函数的参数和模糊控制规则进行综合优化的设计方法,并采用VisualC++6.0编制了相应的软件,该软件已成功地应用于工程中。此外,该软件也可作为遗传优化和模糊控制器设计的辅助教学软件。     

一种基于模糊神经网络的模拟电路故障诊断方法

提出了一种采用小波分析与遗传算法相结合的模糊神经网络对模拟电路进行故障诊断的新方法。该方法采用基于小波分析的主成分分析方法对网络的训练样本进行预处理,提取优化向量后利用遗传算法对模糊神经网络进行训练。对两个模拟电路的诊断实例表明该方法故障覆盖率高,并能有效诊断出同类方法误诊的故障类型。     

一种基于遗传算法的模糊神经网络最优控制

通过对控制系统的过程模拟,提出一种模糊神经网络最优控制方案。离线化部分基于遗传算法,分三阶段实现模糊神经网络控制器结构和参数的优化。在线优化部分通过重构模糊神经网络控制器的去模糊化部分,进一步调整控制规则,实现在线去模糊优化。仿真结果表明该方案优于常模糊控制方案和基于专家经验的模糊神经网络控制方案。     

Sexual Selection for Genetic Algorithms

Genetic Algorithms (GA) have been widely used inoperations research andoptimization since first proposed. A typical GAcomprises three stages, the encoding, theselection and the recombination stages. In thiswork, we focus our attention on the selectionstage of GA, and review afew commonly employed selection schemes andtheir associated scalingfunctions. We also examine common problems andsolution methods forsuch selection schemes.We then propose a new selection scheme inspiredby sexual selectionprinciples through female choice selection, andcompare the performance of this new schemewith commonly used selection methods in solvingsome well-known problems including the Royal RoadProblem, the Open Shop Scheduling Problem andthe Job Shop Scheduling Problem.