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 （Pro） 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.

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.

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.

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.

Knowledge Discovering in Corporate Securities Fraud by Using Grammar Based Genetic Programming

Securities fraud is a common worldwide problem, resulting in serious negative consequences to securities market each year. Securities Regulatory Commission from various countries has also attached great importance to the detection and prevention of securities fraud activities. Securities fraud is also increasing due to the rapid expansion of securities market in China. In accomplishing the task of securities fraud detection, China Securities Regulatory Commission (CSRC) could be facilitated in their work by using a number of data mining techniques. In this paper, we investigate the usefulness of Logistic regression model, Neural Networks (NNs), Sequential minimal optimization (SMO), Radial Basis Function (RBF) networks, Bayesian networks and Grammar Based Genet- ic Programming (GBGP) in the classification of the real, large and latest China Corporate Securities Fraud (CCSF) database. The six data mining techniques are compared in terms of their performances. As a result, we found GBGP outperforms others. This paper describes the GBGP in detail in solving the CCSF problem. In addition, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to generate synthetic minority class examples for the imbalanced CCSF dataset.

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.

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.

基于最优样本和最优属性组合的作业车间调度规则挖掘

作业车间调度问题可使用调度规则解决。为挖掘到高效、准确的调度规则,基于训练样本最优和属性组合最优的核心思想,提出一种基于最优样本与最优属性组合的决策树-遗传算法框架(NDTGA)。该框架在构造训练数据时采用成对比较的方式,在构造属性组合时使用属性原值、差值、对比值等多种组合;在遗传算法的每次寻优过程中,调用决策树挖掘全新的调度规则;最终得到最优训练样本和最优属性组合,进而得到最优的调度规则。通过与经典调度规则和其他机器学习算法的对比实验论证了NDTGA框架挖掘所得调度规则的优越性。

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。

带准备时间的异构并行机调度规则自动设计方法

以大规模定制化的家电行业生产为背景,将家电总装产线的投产排序决策抽象成为一类带准备时间的异构并行机动态调度问题。针对人工调度规则解决动态调度问题简单高效,但场景适应性弱的特点,引入了基于遗传规划(genetic programming,GP)的规则自动设计框架。首先,通过分析家电总装产线生产特征以及优化需求,以最小化平均拖期为优化目标,建立异构并行机调度模型;随后,针对问题特征,构建线体指派-工单排序规则对协同进化的改进型GP算法,并提取线体、工单的特征属性输入GP算法框架以自动设计调度规则。最后,基于某家电企业实际案例数据设计大量算例测试集,通过对比GP算法与人工设计规则在差异化工况场景的实验结果,验证GP算法有效性,并进一步分析了GP算法构造规则受不同生产环境参数的影响。

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。

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。

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。

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GP-PS的分布式加工与装配多级车间调度规则自动设计方法

分布式加工与装配多级制造系统由多个用于加工零件的作业车间和用于装配产品的一般流水车间组成。动态到达的订单涉及多层产品结构,零件需齐备之后才可装配。该类多级车间的管控涉及订单分配、加工和装配任务调度联合决策问题,其关键在于实现两级生产的精准化协同目的。针对分布式加工与装配多级车间调度问题,提出了一种基于GP-PS的分布式加工与装配多级车间调度规则自动设计方法。首先,以最小化订单拖期率为目标,建立了订单分配、加工和装配任务调度联合决策的数学模型;然后,提出了一种改进型遗传规划算法,用以集成进化多级调度规则,设计了一类种群优化机制来避免算法陷入局部收敛,同时嵌入了并行仿真技术,有效减少了训练时间;最后,进行了仿真实验,对改进型遗传算法的性能进行了验证。研究结果表明:人工规则组、标准遗传规划算法及改进型遗传算法得到的订单拖期率分别为6.44%、5.65%、2.67%。基于并行仿真优化的改进型GP算法较数十个优选的人工规则组及标准GP算法生成的最优规则组,能取得更明显的综合性能优势。使用该算法针对DPAMW调度问题自动设计一体化调度的多级规则是可行的、有效的。

基于多重遗传算法的中央空调能效优化

为提高中央空调制冷系统能效比,实现不同工况下最优能效比,提出一种基于多重遗传算法的中央空调制冷系统能效优化方法。该方法通过灰色关联度分析(GRA)筛选出对中央空调能效比影响较大的关键运行参数,利用神经网络建立关键运行参数与能效比间的能效预测模型,通过Apriori关联规则算法获取关键运行参数间的关联规则,将预测模型作为多重遗传算法的适应度函数,关联规则作为遗传算法种群进化的约束条件。分别采用多重遗传算法和传统遗传算法进行能效比优化,结果表明,多重遗传算法比传统遗传算法能效比平均提高了5.75%,优化效果明显。

基于遗传算法的采煤工作面隐患数据关联规则挖掘

分析了采煤工作面的隐患类型和属性,应用遗传算法建立关联规则挖掘模型,并通过文本挖掘与主题挖掘算法挖掘隐患之间的内在关系和隐患之间的关联规则,构建关联规则库。以山东某矿业公司的安全隐患检查记录为数据源,对模型进行验证,并对改进的遗传算法、遗传算法和Apriori算法进行性能对比,表明改进的遗传算法能够有效地挖掘隐患数据的关联规则,有助于加深安全管理人员对隐患数据间蕴含的内在规律的理解,为煤矿安全隐患排查治理提供依据,指导采煤生产的安全管理实践。