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Annual Conference on the Society-of-Instrument-and-Control-Engineers

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

ISBN: (纸本)9784907764289

In this paper we propose a new hierarchical method to extract association rules from large and dense datasets using genetic network programming (GNP) considering a real world database with a huge number of attributes. It uses three ideas. First, the large database is divided into many small datasets. Second, these small datasets are independently processed by the conventional GNP-based mining method (CGNP) in parallel. This level of processing is called Local Level. Finally, new genetic operations are carried out for small datasets considered as individuals in order to improve the number of rules extracted and their quality as well. This level of processing is called Global Level. The amount of small datasets is also important especially for avoiding the overload and improving the general performance;we find the minimum amount of files needed to extract important association rules. The proposed method shows its effectiveness in simulations using a real world large and dense database.

关键词： data mining association rules genetic network programming parallel processing

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Buying and selling stocks of multi brands using genetic network programming with control nodes

Buying and selling stocks of multi brands using genetic netw...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Bao, Zhiguo Mabu, Shigo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9784907764289

A new evolutionary method named "genetic network programming with control nodes, GNPcn" has been proposed. GNPcn represents its solutions as directed graph structures which have some useful features inherently. For example, GNPcn has the implicit memory function which memorizes the past action sequences of agents and GNPcn can re-use nodes repeatedly in the network flow, so very compact graph structures can be made. GNPcn can improve the strategy of buying and selling stocks of multi brands. In this paper, buying and selling stocks of multi brands using GNPcn has been proposed, and its effectiveness is confirmed by simulations.

关键词： buying and selling stocks Candlestick Chart evolutionary computation genetic network programming

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Enhancement of trading rules on stock markets using genetic network programming with Sarsa Learning

Enhancement of trading rules on stock markets using Genetic ...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka Japan

ISBN: (纸本)9784907764289

In this paper, the enhancement of trading rules on stock markets using genetic network programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa learning as the basic algorithm while importance Indices and Candlestick Charts are introduced for efficient stock trading decision-making. Importance indices have been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we develop a new method that can learn appropriate function describing the relation between the value of each technical index and the output of the importance index (IMX). This is an important point that devotes to the enhancement of the proposed GNP-Sarsa algorithm. Third, in order to create more efficient judgment functions to judge the current stock price appropriately, we develop a new way of classifying the candlestick chart body type. To confirm the effectiveness of the proposed method, we also compare the simulation results using GNP-Sarsa with other methods like traditional GNP and Buy&Hold method.

关键词： genetic network programming reinforcement learning Sarsa stock trading model technical index candlestick chart

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Trading Rules on Stock Markets Using genetic network programming with Sarsa Learning 07

Trading Rules on Stock Markets Using Genetic Network Program...

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Annual Conference of genetic and Evolutionary Computation Conference

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781595936974

In this paper, the genetic network programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the buying and selling timing of stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

关键词： genetic network programming Reinforcement Learning Sarsa Stock Trading Model Technical Index Candlestick Chart

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Association Rule Mining for Continuous Attributes using genetic network programming 07

Association Rule Mining for Continuous Attributes using Gene...

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Annual Conference of genetic and Evolutionary Computation Conference

作者： Taboada, Karla Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781595936974

Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get lowest number of intervals. We propose a method using a new graph-based evolutionary algorithm named "genetic network programming (GNP)" that can deal with continues values directly, that is, without using any discretization method as a preprocessing step. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as solutions and is composed of three kinds of nodes: start node, judgment node and processing node. Once GNP is booted up, firstly the execution starts from the start node, secondly the next node to be executed is determined according to the judgment and connection from the current activated node. The features of GNP are described as follows. First, it is possible to reuse nodes; because of this, the structure is compact. Second, GNP can find solutions of problems without bloat, which can be sometimes found in genetic programming (GP), because of the fixed number of nodes in GNP. Third, nodes that are not used at the current program executions will be used for future evolution. Fourth, GNP is able to cope with partially observable Markov processes. In this paper, we propose a method that can deal with continuous attributes, where attributes in databases correspond to judgment nodes in GNP and each continuous attribute is checked whether its value is greater than a threshold value and the association rules are represented as the connections of the judgment nodes. Threshold ai is firstly determined by calculating the mean µi and standard deviation si of all attribute values of Ai. Then, initial threshold ai is selected randomly between the interval [µi - aisi, µi + aisi] where ai is a parameter to determine the range of the interval. Once the threshold ai is selected for all attrib

关键词： continuous attributes genetic network programming association rules mining

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Double-Deck Elevator Group Supervisory Control System Using genetic network programming with Ant Colony Optimization with Evaporation

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2007年第9期11卷 1149-1158页

作者： Yu, Lu Zhou, Jin Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Markon, Sandor Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8030135 Japan Fujitec Co Ltd Prod Dev HQ Hikone Shiga 5228588 Japan

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of genetic Algorithm (GA) and genetic programming (GP), genetic network programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines genetic network programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

关键词： elevator group supervisory control system genetic network programming ant colony optimization hybrid algorithm

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genetic relation algorithm with guided mutation for the large-scale portfolio optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3353-3363页

作者： Chen, Yan Mabu, Shingo Hirasawa, Kotaro Shanghai Univ Finance & Econ Sch Stat & Maragement Shanghai 200433 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan

The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (CRA/G) for the portfolio optimization is proposed in this paper. genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with CRA/G. The results show that CRA/G approach is successful in portfolio optimization. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Portfolio optimization genetic relation algorithm Guided mutation genetic network programming

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An evolutionary approach to rank class association rules with feedback mechanism

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第12期38卷 15040-15048页

作者： Yang, Guangfei Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan Dalian Univ Technol Inst Syst Engn Dalian 116023 Liaoning Peoples R China

In this paper, we propose an evolutionary associative classification method by considering both adjustment of the order of the whole set of rules and refinement of the power of each single rule. We discover an interesting phenomenon that the classification performance could be improved if we import some prior-knowledge to re-rank the association rules, where the prior-knowledge could be some equations generated by combing the support and confidence values with various functions. We make use of genetic network programming to automatically search the equation space for prior-knowledge. In addition to rank the rules by equations globally, we also develop a feedback mechanism to adjust the rules locally, by giving some rewards to good rules and penalties to bad ones. Because the proposed method is based on evolutionary computation, we could gradually refine the power of each rule so that it could affect the classification results more precisely. The experimental results on UCI benchmark datasets show that the proposed method could improve the classification accuracies effectively. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Class association rule genetic network programming Feedback mechanism

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A novel evolutionary method to search interesting association rules by keywords

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第10期38卷 13378-13385页

In this paper, we propose an evolutionary method for directly mining interesting association rules. Most of the association rule mining methods give a large number of rules, and it is difficult for human beings to deal with them. We study this problem by borrowing the style of search engine, that is, searching association rules by keywords. Whether a rule is interesting or not is decided by its relation to the keywords, and we introduce both semantic and statistical methods to measure such relation. The mining process is built on an evolutionary approach, genetic network programming (GNP). Different from the conventional GNP based association rule mining method, the proposed method pays more attention to generate the GNP individuals carefully, which will mine interesting association rules efficiently. After the rules are generated, they will be ranked and annotated by meaningful information, such as similar rules and representative transactions, in order to help the user to understand the rules better. We also discuss how to mine generalized interesting association rules, describing more abstract level of information than the common association rules. In the simulation section, we give some demonstrations of the proposed method using a census data set, which shows a promising way to find the interesting association rules. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Association rule Search engine Semantic annotation genetic network programming

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A Supervised Learning Framework for PCA-based Face Recognition using GNP Fuzzy Data Mining

A Supervised Learning Framework for PCA-based Face Recogniti...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Zhang, Deng Mabu, Shingo Wen, Feng Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781457706530

Traditional PCA-based face recognition algorithms usually have low performance in the complicated illumination database. There are two reasons. One is that the number of classes is large compared with other classification problems. The other is that the data in the PCA domain distributes in a narrow space and overlaps frequently. This paper presents a novel supervised learning framework for PCA-based face recognition using genetic network programming (GNP) fuzzy data mining (GNP-FDM). In the proposed framework, a face recognition oriented genetic-based clustering algorithm (GCA) is used to reduce the number of classes and overlaps in the recognition. And, a fuzzy class association rules (FCARs) based classifier is applied to mine the inherent relationships between eigen-vectors and to improve the recognition accuracy. Experimental results on the extended Yale-B database indicate that the proposed supervised learning framework has higher accuracy compared with the traditional PCA-based methods.

关键词： Complicated Illumination Database Face Recognition Fuzzy Data Mining genetic network programming Principal Component Analysis

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