Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (Pc) and mutation probability (Pm) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.

An Inductive Method with Genetic Algorithm for Learning Phrase－structure－rule of Natural Language

This paper describes an Inductive method with gnnetic search which learns attribute based phraserllle of natural laguage from set of preclassified examples. Every example is described with some attributes/values. This algorithm takes an example as a seed, generalizes it by genetic process, and makes it cover as many examples as possible. We use genetic operator in population to perform a probabilistic parallel search in rule space and it will reduce greatly possibe rule search space compared with many other inductive methods. In this paper, we give description of attribute, word, dictionary and rule at first. then we describe learning algoritm and genetic search Proctess, and at last, we give a computing method abour quility of roule C(r).

Analysis of Distributed and Adaptive Genetic Algorithm for Mining Interesting Classification Rules

Distributed genetic algorithm can be combined with the adaptive genetic algorithm for mining the interesting and comprehensible classification rules.The paper gives the method to encode for the rules,the fitness function,the selecting,crossover,mutation and migration operator for the DAGA at the same time are designed.

基于ER Rule的多分类器汽车评论情感分类研究

该文针对汽车评论语料的情感二分类问题,提出一种基于证据推理规则的多分类器融合的情感分类方法。在情感特征构建方面,通过实验对比不同特征模型对分类结果的影响,并改进传统的TFIDF权重计算方法。同时,在此基础上使用ER Rule融合不同分类器进行文本情感极性分析,并考虑各分类器的权重和可靠度。最后,爬取汽车网站上的评论数据对上述方法进行测试,并用公开的中文酒店评论语料数据进行了验证,结果表明该方法能够有效集成不同分类器的优点,与传统机器学习分类算法相比,其结果在Recall,F1值和Accuracy三个指标上得到了提高,与目前流行的深度学习算法和集成学习算法相比,其结果总体占优。

Anomaly Classification Using Genetic Algorithm-Based Random Forest Modelfor Network Attack Detection

Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks.Network-based intrusion detection systems(NIDSs)using machine learning(ML)methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks.Among several ML methods,random forest(RF)is a robust method that can be used in ML-based network intrusion detection solutions.However,the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy.Therefore,optimal parameter selection is a real problem in RF-based anomaly classification of intrusion detection systems.In this paper,we propose to use the genetic algorithm(GA)for selecting the appropriate values of these two parameters,optimizing the RF classifier and improving the classification accuracy of normal and abnormal network traffics.To validate the proposed GA-based RF model,a number of experiments is conducted on two public datasets and evaluated using a set of performance evaluation measures.In these experiments,the accuracy result is compared with the accuracies of baseline ML classifiers in the recent works.Experimental results reveal that the proposed model can avert the uncertainty in selection the values of RF’s parameters,improving the accuracy of anomaly classification in NIDSs without incurring excessive time.

Genetic Algorithm for Scattered Storage Assignment in Kiva Mobile Fulfillment System

Scattered storage means an item can be stored in multiple inventory bins. The scattered storage assignment problem based on association rules in Kiva mobile fulfillment system is investigated, which aims to decide the pods for each item to put on so as to minimize the number of pods to be moved when picking a batch of orders. This problem is formulated into an integer programming model. A genetic algorithm is developed to solve the large-sized problems. Computational experiments and comparison between the scattered storage strategy and random storage strategy are conducted to evaluate the performance of the model and algorithm.

Quality of Service Routing Strategy Using Supervised Genetic Algorithm

A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates the convergent process up to 20 times.

Prediction method of rock burst proneness based on rough set and genetic algorithm

A new method based on rough set theory and genetic algorithm was proposedto predict the rock burst proneness. Nine influencing factors were first selected, and then,the decision table was set up. Attributes were reduced by genetic algorithm. Rough setwas used to extract the simplified decision rules of rock burst proneness. Taking the practical engineering for example, the rock burst proneness was evaluated and predicted bydecision rules. Comparing the prediction results with the actual results, it shows that theproposed method is feasible and effective.

Analytical Solution for the Time-Dependent Emden-Fowler Type of Equations by Homotopy Analysis Method with Genetic Algorithm

In this paper, Homotopy Analysis method with Genetic Algorithm is presented and used to obtain an analytical solution for the time-dependent Emden-Fowler type of equations and wave-type equation with singular behavior at x = 0. The advantage of this single global method employed to present a reliable framework is utilized to overcome the singularity behavior at the point x = 0 for both models. The method is demonstrated for a variety of problems in one and higher dimensional spaces where approximate-exact solutions are obtained. The results obtained in all cases show the reliability and the efficiency of this method.

The Use of Multi-Objective Genetic Algorithm Based Approach to Create Ensemble of ANN for Intrusion Detection

Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Consistency between traditional Chinese medicine constitution-based classification and genetic classification

Background:We studied the consistency between two classification systems for categorizing patients:traditional Chinese medicine(TCM)constitution-based methods,versus genetic clustering.Genetic classification in constitutional identification was also evaluated.Methods:A TCM physician evaluated the constitution of each patient,according to four examinations(inspection,auscultation-olfaction,interrogation,and palpation).Those who met the criteria for Yang-deficient,Yin-deficient,and balanced constitutions were enrolled in the study.Peripheral blood samples were obtained from the participants,and peripheral blood mononuclear cells were separated from the samples within 2 hours.Total RNA extraction from the white blood cells was performed;and an Affymetrix HG-U133 Plus2.0 array was used to determine the peripheral blood gene expression profiles.The samples were classified using a support vector machine genetic classifier,and the“leave-one-out”method was used for validation.Results:The global gene expression profiles of 32 samples were grouped into three categories,and the samples in each of the gene categories corresponded with the three constitution categories.The three constitution types were distinguished using the genetic classifier with 165 genes.The accuracy of the prediction classification was greater than 95%using mathematical method.Conclusions:Participants with Yin-deficient,Yang-deficient,and balanced constitutions have varying physical characteristics and gene expression patterns.Additionally,the results from TCM constitution classification matched those obtained by genetic classification.Finally,our preliminary gene classifier distinguishes among Yin-deficient,Yang-deficient,and balanced constitutions,and provides a methodological basis for identifying the different constitutions.

Ad Hoc Network Hybrid Management Protocol Based on Genetic Classifiers

The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.

蜜蜂线粒体基因组多态性应用研究进展

线粒体基因组(Mitochondrial genome)能够自我复制且遵循严格的母系遗传,是哺乳动物、昆虫等物种进化生物学、保护遗传学研究的重要分子标记之一。利用线粒体DNA作为遗传标记有助于探究蜜蜂群体遗传多样性、母系起源及不同种群间的系统发育关系,相关研究主要集中于COI、COII、tRNA^(Leu)-COII、Cytb等单个线粒体基因序列片段,线粒体基因组全序列的应用较少。蜜蜂属现有9个种的线粒体基因组全序列测序工作均已完成,为蜜蜂遗传学研究提供了基础数据。本文综述了线粒体基因组在蜜蜂中的研究进展,阐述蜜蜂线粒体基因组结构特征及其在蜜蜂遗传多样性、起源进化、分类鉴定等研究的应用进展,同时分析蜜蜂线粒体基因组的应用意义并展望其未来发展方向,以期为我国蜜蜂遗传资源保护与利用提供信息参考。

基于Petri网和改进遗传算法的多资源调度问题

针对混流装配线工序加工资源需求多样、工艺复杂、装配工期长等问题,采用Petri网和改进遗传算法对该问题进行优化求解。建立混流装配线赋时库所Petri网(timed place Petri net, TPPN)调度模型,基于模型激发序列,采用基于工序的编码方式进行染色体编码;采用精英保留策略选择优异个体,改进遗传算法的交叉、变异操作,用改进后的遗传算法求解混流装配线调度问题。通过对比案例及实例数据计算结果验证了方案的有效性。

一种作战推演模型行为优化方法

作战推演中实体模型决策依赖于行为规则,为解决作战行为规则构建难、现有规则条件判断不全面的问题,提出一种作战推演模型行为优化方法。针对战场态势模糊和不确定的特点,构建了作战推演模型决策框架,基于模糊推理系统搭建以行为规则库为核心的决策模块,对遗传算法进行改进,借助作战推演环境训练实现行为规则库的优化。通过实验分析证明了该方法的可行性和有效性。

基于AdaBoost算法的新能源汽车电机异常故障检测

新能源汽车的电机系统包含许多复杂的部件和子系统,部件之间的相互作用使得异常故障的检测变得复杂,而电机异常故障检测主要采用人工检测方式,即通过耳朵听声音,用眼睛观察,用手触摸找出故障位置,导致故障检测精度较低。因此,提出AdaBoost算法下新能源汽车电机异常故障检测方法。通过传感器采集电机信号,采用距离相似度、模糊隶属度函数提取信号特征,借助遗传算法的编码操作、交叉操作及其变异操作获取关键信号特征,运用自适应增强(Adaptive Boosting,AdaBoost)算法将信号特征分成正常信号和异常故障,以此实现对新能源汽车电机异常故障检测。实验结果表明,所提算法电机异常故障检测精度高,且耗时短。

区间水优先的水库群引供水调度规则研究

【目的】随着跨流域调水工程不断地发展与完善,没有调节能力的闸坝逐渐与水库共同参与到调度中来,对需要考虑多流域丰枯特性和多水库调节能力的调度规则制订提出了更大的挑战。【方法】为此,提出在调水中相对于水库优先利用区间水的原则,以此构建系统总弃水量最小的调度模型,利用改进的遗传算法求解得到调度规则。并且,以石湖-龙湾-碧流河水库与黑鱼汀闸坝联合的引洋入连引调水工程作为研究实例进行对比。【结果】结果显示,相比于常规调度,优先利用区间水的调度规则方案规避了系统深度破坏,满足95%保证率的城市供水需求,多年平均引水量增加了0.79亿m^(3),系统年均弃水量减少了2.1亿m^(3)。【结论】该研究方法较好地提高了区间水的利用率及系统的用水效率,可为有闸坝参与的跨流域调水系统的调度提供科学参考和技术支撑。

面向客户个性化产品配置的关联规则挖掘研究

针对个性化产品配置中难以获取隐性配置知识等问题,提出了基于关联规则挖掘方法来获取产品配置规则的方法,从而便于在配置过程中对客户进行个性化的推荐。基于产品配置的历史销售数据,应用基于双层遗传算法来实现了关联规则挖掘算法,并设计了遗传算法的编码表示和算子操作。最后,以平板电脑的客户配置案例为例,举例说明了所提出方法的有效性。实验表明,与经典的Apriori算法相比,所提出的方法能够自适应地获得规则支持度和置信度的阈值,避免了Apriori人为设置阈值所带来的不足之处,从而能够适用于大数据环境下产品的个性化推荐。

基于改进遗传算法和DBSCAN聚类的学习数据深度挖掘方法

为了从在线学习大数据中提取有用信息,实现自适应特征提取和聚类,提出了基于改进模糊遗传算法和DBSCAN聚类的细粒度学习数据挖掘方法。通过在信息管理平台中应用数据挖掘技术,将学习表现评估转换为文本分类问题,基于动态数据分析细粒度的知识获取结果。所提改进的遗传算法自动提取出文本中的最优特征集,利用模糊规则关联测试内容与知识点。最后,利用基于密度的聚类算法得到每个知识点的个体和整体测试结果。实验结果表明,所提方法能够自动处理大量数据,全面准确地分析测试结果中不同知识点的掌握程度,有助于信息管理平台数据的二次开发和深入挖掘。