Genetic network programming with subroutines for automatic program generation

In this paper, a subroutine mechanism is introduced in genetic network programming for automatic program generation (GNP‐APG). The proposed method named GNP with subroutines for APG (GNPsr‐APG) is an extension of the algorithm of GNP‐APG, where hierarchy programs are generated: in other words, programs that contain a main function and subroutines are generated. The proposed method automatically defines the main function and use of the potentially useful subroutines during evolution. By using subroutines, a complex program can be decomposed to several simple programs which are obtained more easily. Moreover, these subroutines are called many times from a main program, which results in reducing the size of the program significantly. The simulation results verify that the proposed method can improve the performance of GNP‐APG and reduce the size of the program. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于定制遗传算法考虑配电网多种拓扑可观性的PMU优化配置

同步相量测量单元(PMU)可以为配电网提供相量数据以提高可观性。考虑因配电网节点数目多但投资成本少造成的PMU供需不平衡,提出了以固定PMU数目为约束条件的优化配置模型。目标函数在最大化可观性节点数目的前提下,最大化网络量测冗余度。模型中考虑了多种拓扑结构的影响,并通过引入零注入节点、节点注入功率和支路功率等量测数据提高可观性。提出了一种定制遗传算法来求解模型,通过定制交叉和变异操作,保证所有个体为可行解。最后,给出了基于最优方案的PMU配置顺序。通过对IEEE标准节点系统进行仿真计算,验证了所提方法的可行性和有效性。     

A portfolio selection model using genetic relation algorithm and genetic network programming

In this paper, new evolutionary computation methods named genetic relation algorithm (GRA) and genetic network programming (GNP) have been applied to the portfolio selection problem. The number of brands in the stock market is generally very large, therefore, techniques for selecting the effective portfolio are likely to be of interest in the financial field. In order to pick up the most efficient portfolio, the proposed model considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in GRA. The algorithm evaluates the relationships between stock brands using a specific measure of strength and generates the optimal portfolio in the final generation. Then, the selected portfolio is further optimized by the stock trading model of GNP. In a sense, the proposed model is an integrated intelligent model. A comprehensive analysis of the results is provided, and it is clarified that the proposed model can obtain much higher profits than other traditional methods. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Acquisition of optimal attribute subset through genetic algorithm using GNP‐based class association rule mining

Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset for the genetic network programming (GNP) based class association rule mining has been proposed, and this attribute selection process using genetic algorithm (GA) leads to a higher accuracy for classification. Class association rule mining through GNP is conducted with a small subset of data rather than the original large number of attributes; thus simple but important rules are obtained for classification while the local optimal problem is avoided. Simulation results with educational data show that the classification accuracy is largely improved from 52.73 to 74.54%, when classification is made using the optimal attribute subset. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

MBFP generalized association rule mining and classification in traffic volume prediction

Genetic network programming (GNP)‐based time‐related association rules mining method provides a useful mean to investigate future traffic volume of road networks and hence helps us to develop traffic navigation system. Further improvements have been proposed in this paper about the time‐related association rule mining using generalized GNP with multibranches and full‐paths (MBFP) algorithm. For fully utilizing the potential ability of GNP structure, the mechanism of generalized GNP with MBFP is studied. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time‐related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time‐related association rules is described, and experimental results are presented considering a traffic prediction problem. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An efficient class association rule‐pruning method for unified intrusion detection system using genetic algorithm

Genetic network programming (GNP)‐based class association rule mining has been demonstrated to be efficient for misuse and anomaly detection. However, misuse detection is weak in detecting brand new attacks, while anomaly detection has a defect of high positive false rate. In this paper, a unified detection method is proposed to integrate misuse detection and anomaly detection to overcome their disadvantages. In addition, GNP‐based class association rule mining method extracts an overwhelming number of rules which contain much redundant and irrelevant information. Therefore, in this paper, an efficient class association rule‐pruning method is proposed based on matching degree and genetic algorithm (GA). In the first stage, a matching degree‐based method is applied to preprune the rules in order to improve the efficiency of the GA. In the second stage, the GA is implemented to pick up the effective rules among the rules remaining in the first stage. Simulations on KDDCup99 show the high performance of the proposed method. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Rule pool updating through Sarsa‐learning to improve adaptability in changing environments

Genetic network programming (GNP) is a new evolutionary algorithm using the directed graph as its chromosome. A GNP‐based rule accumulation (GNP‐RA) method was proposed previously for multiagent control. However, in changing environments where new situations appear frequently, the old rules in the rule pool become incompetent for guiding the agent's actions, and therefore updating them becomes necessary. This paper proposes a more robust rule‐based model which can adapt to the environment changes. In order to realize this, Sarsa‐learning is used as a tool to update the rules to cope with the unexperienced situations in new environments. Furthermore, Sarsa‐learning helps to generate better rules by selecting really important judgments and actions during training. In addition, the ε‐greedy policy of Sarsa enables GNP‐RA to explore the solutions space sufficiently, generating more rules. Simulations on the tile world problem show that the proposed method outperforms the previous ones, namely GP and reinforcement learning. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Association Rule Mining for Continuous Attributes using Genetic Network Programming

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real‐world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph‐based evolutionary algorithm named ‘genetic network programming (GNP)’ that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ² test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real‐life database suggest that the proposed method provides an effective technique for handling continuous attributes. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于随机机会约束规划的有源配电网多目标规划

含分布式新能源配电网规划均采用被动、保守接入分布式新能源的规划方法,固然保证了配电网安全,但并没有反映分布式新能源的出力特征,因而造成不必要的配电网建设投资。为了解决这一问题,采用基于随机机会约束规划的有源配电网规划方法,将有源配电网规划中必须满足的硬性约束条件转变为较高置信度的软约束形式,同时,在模型中引入反映经济效益的投资成本、网络损耗以及反映配电网供电安全性的电压偏移度这3个目标函数作为优化对象,形成了有源配电网规划的多目标随机机会约束规划模型。采用结合量子法改进的非支配排序多目标优化遗传算法（non-dominated sorting genetic algorithm2, NSGA-2）求解获得非劣解帕累托前沿,在此基础上,运用逼近理想解排序法（technique for order preference by similarity to ideal solution,TOPSIS）对非劣解排序得到最优方案。最后,以57节点的配电网网络为算例,验证了方法的可行性、有效性。     

Optimal conductor selection for radial distribution systems

In this paper, an algorithm is proposed for selecting the optimum size of conductors of feeder sigments of radial distribution networks. The optimal size of conductor determined by load flow method is applied to the optimal distance radial distribution network. The conductor, which is determined by the proposed method, will maximize the total saving in cost of conducting material and cost of energy losses and maintain acceptable voltage levels in radial distribution systems. An attempt has been made to reduce the losses in the existing radial distribution networks by optimizing the size of the branch conductor. The effectiveness of the proposed method is illustrated with suitable examples.     

基于凹陷域分析的电压暂降监测点优化配置

提出一种基于凹陷域分析的电压暂降监测点优化配置的新方法,以监测点数目最小为目标函数,以全网敏感性负荷节点电压暂降可观测性为约束条件,利用整数线性规划方法,通过MATLAB编程求取符合要求的最少数目监测点。在构造电压暂降幅值矩阵的计算中,考虑到了每一个节点故障可能引发系统其余节点电压暂降的情况。在IEEE 39节点系统上进行仿真计算,成功求得了保证全网敏感性负荷节点电压暂降可观测的最少数目的监测点。     

基于模糊微分演化算法的配电网综合规划

针对配电网综合规划问题提出一种模糊微分演化算法。设计了模糊控制器来自适应调整演化参数中的变异缩放因子和交叉概率常数,避免了迭代陷入局部最优解,同时加快了演化速度。为避免盲目搜索设计了一种保持配电网辐射约束的初始化染色体编码方法,并对演化过程产生的不可行个体的修复代价进行估计,尽可能修复成可行个体。在潮流校验时,将网络拓扑分析得到的母线-支路编号直接用于潮流计算,并尽量将结线分析限制在结构发生变化的馈线及与该馈线相关的电气岛内。对IEEE 50节点算例系统的仿真分析表明,文中算法能有效提高收敛速度并明显改善收敛性能。     

直流配电网断路器优化配置方法

直流断路器的优化配置对提高直流配电网可靠性、降低用户停电损失具有重要意义。基于最小割集法提出了直流负荷点区域划分方法,建立了计及电力电子设备功能故障的停电损失费用计算模型。以开关设备投资运行费用和停电损失费用之和最低为优化目标,配置断路器数目为阶段数,配置断路器位置组合为状态,针对直流断路器造价随技术发展下降空间较大的特点,提出了一种考虑价格变化的直流断路器配置动态规划算法。以手拉手型直流配电网为例进行了计算分析,结果表明该方法能够对直流断路器价格进行区间划分,并得到各区间所对应的最优配置方案。     

风光联合优化配置的多目标机会约束规划方法

针对风速、太阳辐射、负荷的随机性和相关性,综合考虑成本、网损和电压质量,应用蒙特卡洛模拟法和机会约束规划法建立了风力发电机组和光伏方阵两种分布式可再生能源接入现有配电网的多目标优化配置模型。在蒙特卡洛法的基础上,提出了多区间划分、建立概率分布的方法,从而减少抽样次数。在求解模型过程中,首先利用多目标微分进化算法进行全局寻优,得到一组pareto最优解集,然后采用基于熵的模糊多属性决策方法选取折衷最优解。IEEE-33节点配电系统规划结果验证了模型的合理性和方法的有效性。     

Network Intrusion Detection Using Class Association Rule Mining Based on Genetic Network Programming

Because of the expansion of the Internet in recent years, computer systems are exposed to an increasing number and type of security threats. How to detect network intrusions effectively becomes an important technique. This paper proposes a class association rule mining approach based on genetic network programming (GNP) for detecting network intrusions. This approach can deal with both discrete and continuous attributes in network‐related data. And it can be flexibly applied to both misuse detection and anomaly detection. Experimental results with KDD99Cup and DARPA98 database from MIT Lincoln Laboratory shows that the proposed method provides a competitive high detection rate (DR) compared to other machine learning techniques. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

计及节点-线路重要度的配网实时健康状态评估

随着大量分布式光伏的接入，配电网运行状态变得更为复杂。为准确的评估配电系统实时健康状态，提出一种计及网络节点与线路重要度的配电网实时健康状态评估方法。首先，考虑光伏接入网络最优潮流、负荷重要性和某节点停运后的负荷损失对LeaderRank算法进行改进，考虑某节点停运后的功率损失和节点LR值对负荷矩算法进行改进，利用改进的电气LeaderRank算法和线路负荷矩算法对配电网中节点与线路重要度进行评估；然后，基于配电设备运行的电气量与非电气量参数分别计算配电节点与线路的健康指数。综合考虑配电网中节点与线路的健康度和重要度计算配电网的健康指数，判断配电网的健康状态。最后，以某地10 kV配电网为例，得到其健康指数为2.266 3,处于一般缺陷状态，适当提高该配电网中分布式光伏渗透率，其健康状态有所改善，证明所提方法的有效性与合理性。     

Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm

In this paper, a method which employs a Modified Teaching–Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Generation (DG) units in distribution systems. For the sake of clarity, and without loss of generality, the objective function considered is to minimize total electrical power losses, although the problem can be easily configured as multi-objective (other objective functions can be considered at the same time), where the optimal location of DG systems, along with their sizes, are simultaneously obtained. The optimal DG site and size problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used by researchers to solve this problem because of their independence from type of the objective function and constraints. Recently, a new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations. A comparison between the proposed algorithm and a brute force method is performed. Besides this, it has also been carried out a comparison using several results available in other articles published by others authors. Numerical results for two test distribution systems have been presented in order to show the effectiveness of the proposed approach.     

配电网络优化规划的基因算法

提出了一种适用于配电网辐射型结构特点的网络优化基因算法。此算法利用配电网辐射状的特点,选择与网络结构相关的特殊变量作为为量,使生成的网络自然呈辐射状,且算法中优化变量的个与负荷节点的个数相同,提高 搜索寻优的效率。     

A genetic algorithm for the optimal sequential partitioning problem

The optimal sequential problem is defined as the problem of finding the minimum cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relationships among the elements are satisfied. A heuristic algorithm based on tabu search has been proposed for this problem [2]. However, there is a tendency for the solutions obtained by tabu search to become trapped in bad local optima in parallel graphs with random edge costs. In this paper we present a genetic algorithm for the optimal sequential partitioning problem. We develop an effective two‐point partial order crossover satisfying sequential conditions, which preserve better blocks that have a larger sum of edge costs. In this crossover we introduce the roulette selection method to escape local optima. We also assess the effectiveness of the algorithm. The results show that this proposed algorithm outperforms any other algorithm using tabu search in terms of solution quality. © 2001 Scripta Technica, Electr Eng Jpn, 135(4): 43–51, 2001     

应用进化规划变异算子的配电网重构算法

针对进化规划算法普遍存在的进化过程缓慢和进化后期难以平稳收敛等问题,考虑到配电网重构功率损耗最小和负荷均衡2个目标,提出一种新的进化规划变异算子。为保证变异产生的新个体积极地向最优个体靠拢,一方面使变异算子的拓扑调整次数与进化代数成反比,另一方面在拓扑调整过程中增加待合开关选择算子和待分开关选择算子。同时,为提高整个配电网重构算法的计算速度,研究了功率损耗、二次负荷矩、待合开关选择算子和待分开关选择算子的简化计算方法。所提方法的可行性在河南省商丘供电局的10 kV配电网运行优化与辅助决策系统上得到了验证。