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network Intrusion Detection Using Class Association Rule Mining Based on genetic network programming

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2010年第5期5卷 553-559页

作者： Chen, Ci Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

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. (C) 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： network intrusion detection class association rule mining genetic network programming

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A portfolio optimization model using genetic network programming with control nodes

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第7期36卷 10735-10745页

作者： Chen, Yan Ohkawa, Etsushi Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Waseda Univ Informat Prod & Syst Res Ctr Wakamatsu Ku Fukuoka 8080135 Japan

Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named "genetic network programming" (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands portfolio optimization model based on genetic network programming with control nodes is presented. This method makes use of the information from technical indices and candlestick chart. The proposed optimization model, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than these methods. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Portfolio optimization genetic network programming Control node Reinforcement learning

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A Traffic-Flow-Adaptive Controller of Double-Deck Elevator Systems using genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2008年第6期3卷 703-714页

作者： Zhou, Jin Yu, Lu Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Markon, Sandor Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Fujitec Co Ltd Prod Dev HQ Shiga Japan

The double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all the traffic flows vary continuously during a day, an optimized controller of DDES is required to adapt to the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using genetic network programming (GNP) based on Our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A new traffic flow judgment part was introduced into the GNP framework of DDES controller in this paper, and the different parts of GNP were expected to be functionally localized by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the working time of a typical office building. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： double-deck elevator system genetic network programming evolutionary computation

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Evolving asset selection using genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第2期7卷 174-182页

作者： Parque, Victor Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

As global financial innovation opens innumerable risks and opportunities, a global view of the asset allocation brings advantages in risk diversification for investments. We propose a novel framework for asset selection under global diversification principles using genetic network programming. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and offers competitive advantages against the conventional methods in finance and computational fields. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： genetic network programming asset selection value and growth evolutionary finance

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A Portfolio Selection Model Using genetic Relation Algorithm and genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2011年第5期6卷 403-413页

作者： Chen, Yan Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China

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. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： portfolio selection genetic relation algorithm genetic network programming

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Medical Association Rule Mining Using genetic network programming

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2008年第2期91卷 46-54页

作者： Shimada, Kaoru Wang, Ruoichen Hirasawa, Kotaro Furuzuki, Takayuki Waseda Univ Tokyo Japan

An efficient algorithm for building a classifier is proposed based on an important association rule mining using genetic network programming (GNP). The proposed method measures the significance of the association via the chi-squared test. Users can define the conditions of important association rules for building a classifier flexibly. The definition can include not only the minimum threshold chi-squared value, but also the number of attributes in the association rules. Therefore, all the extracted important rules can be used for classification directly. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure as genes. Instead of generating a large number of candidate rules, Our method can obtain a sufficient number of important association rules for classification. In addition, our method suits association rule mining from dense databases such as medical datasets, where many frequently occurring items are found in each tuple. In this paper, we describe an algorithm for classification using important association rules extracted by GNP with acquisition mechanisms and present some experimental results of medical datasets. (C) 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 46-54, 2008;Published online in Wiley InterScience (***). DOI 10. 1002/ecj.10022

关键词： evolutionary computation genetic network programming data mining association rule classification

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Analysis of Energy Consumption of Elevator Group Supervisory Control System Based on genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2011年第5期6卷 414-423页

作者： Yu, Lu Mabu, Shingo Hirasawa, Kotaro Ueno, Tsuyoshi Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Cent Res Inst Elect Power Ind Kita Ku Komae Tokyo 2018511 Japan

Elevator Group Supervisory Control System (EGSCS) is a traffic system, which provides the transportation services for passengers in modern buildings. As the elevator systems include uncertainty due to the future arrival of the passengers, it difficult to model, analyze, and optimize the elevator group supervisory control system. Recently, artificial intelligence technology has been used in such complex systems. genetic network programming(GNP), a graph-based evolutionary method extended from genetic algorithm and genetic programming, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as one of the criteria of the elevator operations. The elevators with maximum energy efficiency are therefore required. In this paper, the GNP is used to solve EGSCS with energy consumption (EC). Moreover, the idle car assignment has been embedded in the proposed method. Finally, the simulations show that some factors should be introduced into GNP in order to deal with the higher EC in the light traffic of the elevator systems. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： Elevator Group Supervisory Control System energy consumption genetic network programming

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Association rule mining for continuous attributes using genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2008年第2期3卷 199-211页

作者： Taboada, Karla Gonzales, Eloy Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

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 chi(2) 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. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： genetic network programming association rules mining continuous attributes

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Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第16期39卷 12349-12357页

作者： Mabu, Shingo Tjahjadi, Andre Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation genetic network programming Reinforcement learning Adaptability Khepera robot

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Combination of genetic network programming and knapsack problem to support record clustering on distributed databases

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 46卷 15-23页

作者： Wedashwara, Wirarama Mabu, Shingo Obayashi, Masanao Kuremoto, Takashi Yamaguchi Univ Grad Sch Sci & Engn Ube Yamaguchi 7558611 Japan

This research involves implementation of genetic network programming (GNP) and standard dynamic programming to solve the knapsack problem (KP) as a decision support system for record clustering in distributed databases. Fragment allocation with storage capacity limitation problem is a background of the proposed method. The problem of storage capacity is to distribute sets of fragments into several sites (clusters). Total amount of fragments in each site must not exceed the capacity of site, while the distribution process must keep the relation (similarity) between fragments within each site. The objective is to distribute big data to certain sites with the limited amount of capacities by considering the similarity of distributed data in each site. To solve this problem, GNP is used to extract rules from big data by considering characteristics (value ranges) of each attribute in a dataset. The proposed method also provides partial random rule extraction method in GNP to discover frequent patterns in a database for improving the clustering algorithm, especially for large data problems. The concept of KP is applied to the storage capacity problem and standard dynamic programming is used to distribute rules to each site by considering similarity (value) and data amount (weight) related to each rule to match the site capacities. From the simulation results, it is clarified that the proposed method shows some advantages over the conventional clustering algorithms, therefore, the proposed method provides a new clustering method with an additional storage capacity problem. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： genetic network programming Database clustering Knapsack problem Record clustering

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