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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / International Joint Conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE-CEC) / IEEE World Congress on Computational Intelligence (IEEE-WCCI)

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

ISBN: (纸本)9781467315098

The standard methodology in machine learning is to learn one problem at a time. But, many real-world problems are complex and have multitasks, and it is a bit hard to learn them well by one machine learning approach. So, the simultaneous learning of several tasks has been considered, that is, so-called multitask learning. This paper describes a new approach to the autonomous agent problem using the multitask learning scheme based on genetic network programming (GNP), called ML-GNP, where each GNP is used to learn one corresponding task. ML-GNP has some charateristics, such as distribution, interaction and autonomy, which are helpful for learning multitask problems. The experimental results illustrate that ML-GNP can give much better performance than learning all the tasks of the problem by one GNP algorithm.

关键词： Multitasks Learning Approach genetic network programming network programming Intelligent agents PARALLEL PROCESSING (COMPUTERS) economic indicators Religious Missions Learning Autonomy

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genetic network programming with Credit

Genetic Network Programming with Credit

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SICE Annual Conference

作者： Wei Xu Lutao Wang Shingo Mabu Kotaro Hirasawa Graduate school of Information Production and System Waseda University

ISBN: (纸本)9781467322591

The purpose of this paper is to study how to improve the performance of genetic network programming (GNP) by adding a credit mechanism and its application to trades on stock markets. GNP is one of the evolutionary computations having directed graph structures. Because of this unique structures, GNP could be more effective for gaining profits in stock markets than the other methods. However, the structure of GNP has a high possibility to contain redundant nodes. These nodes will make negative effects to the whole structure. The proposed method will use a new branch named credit branch for making those redundant nodes pruned. Pruning is determined by both evolution and Sarsa-Learning of GNP. This algorithm can efficiently improve the performance of GNP. When the credit branch is chosen, the node will be skipped and the content will also be neglected. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness. The results show that the proposed approach can provide better result than conventional GNP without credit branch.

关键词： genetic network programming Reinforcement learning Credit branch Stock trading model Technical index

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A Bidding Strategy using genetic network programming with Adjusting Parameters for Large-scale Continuous Double Auction

A Bidding Strategy using Genetic Network Programming with Ad...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Chuan Yue Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781467317139

Along with the explosive development of electronic commerce, trading goods online becomes much more popular and the trading volume over internet has been increased hugely. Concentrating particularly on continuous double auction (CDA), which is an efficient market mechanism, this paper studied and discussed a genetic network programming (GNP) based bidding strategy with adjusting parameters for autonomous software agents in agent-based large-scale CDAs (GNP-AP). GNP is one of the evolutionary computations, and the individuals with directed graph structures represents the potential bidding strategies. Combined with the heuristic control rules, each individual can collect and judge the auction information, and then choose the decision-making transition depending on the judgment results. The parameters of CDAs to select the right decision are adjusted during the evolution in order to get more profits for large-scale CDAs. In the experiments, we studied and discussed the performance of the proposed bidding strategies and compared it with other classic bidding strategies and the previous strategy developed by GNP with rectifying node (GNP-RN) in the large-scale CDA under different settings.

关键词： genetic network programming Bidding strategy Continuous double auction Large-scale CDA adjustable parameter network programming Intelligent agents economic indicators Congenital Dyserythropoietic Anemia large sizes market mechanism

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The Optimization and Enhancement of network Intrusion Detection through Fuzzy Association Rules 6

The Optimization and Enhancement of Network Intrusion Detect...

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6th International Conference on Technical Education (ICTechEd) / 11th National Conference on Technical Education (NCTechEd)

作者： Boonyopakorn, Pongsarun KMUTNB Fac Informat Technol Dept Data Commun & Networking Bangkok Thailand

ISBN: (纸本)9781728105390

The objective of this paper is to analyze network security using a modification of fuzzy logic and association rule data mining base on genetic network programming (GNP) evaluations. The application of fuzzy set theory together with GNP combined will increase the ability to detect threats on a computer network or the Internet. The proposed method is an extension of the intrusion detection method so that it can detect and distinguish known and unknown normal intrusion using the GNP workflow method to obtain the required number of rules. In the experiment, 10% data sets of KDD cup 99 data sets were used to teach and test the developed system's performance. The test results from similar algorithms were then compared.

关键词： intrusion detection fuzzy association rules genetic network programming

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A league championship algorithm equipped with network structure and backward Q-learning for extracting stock trading rules

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APPLIED SOFT COMPUTING 2018年 68卷 478-493页

作者： Alimoradi, Muhammad Reza Kashan, Ali Husseinzadeh Tarbiat Modares Univ Dept Ind & Syst Engn Tehran Iran

Buying and selling stocks are the main activities of investment in the financial markets. During several years, financial experts have designed various indices and indicators to develop intelligent methods to decide whether buy or sell or no trade a specific stock. In this paper, the league championship algorithm (LCA) is adapted and equipped with a network structure (which provides the implicit memory function, compact structure and gives the ability of reusing nodes) for stock trading rule extraction process. The proposed algorithm is able to extract and save various stock trading rules for various kinds of stock market conditions. Each individual in LCA makes role as a sport team which could learn from weaknesses and strengths of others to enhance its performance. The network structure used for each individual is equipped with reinforcement learning to strength the searching power and extracting better rules. The backward Q-learning (BQL), which is a combination of Sarsa learning and Q-learning algorithms, is also hybridized with LCA to increase the learning speed to extract better rules. A trend clustering technique is also developed to determine uptrends and downtrends and to remedy delay in the trend direction reflection along with moving average of stock prices. Finally, to use the trading rules extracted and saved in the rule pools, a new approach through average matching degree is introduced which could create trinary signals of buying, selling, and no trade. The effectiveness of the proposed approach is tested on a set of 20 companies, which have high liquidity and large market capitalization, chosen from various fields of Tehran Stock Exchange. The used historical price data was adjusted through factors of dividends and capital increase. Computational tests and simulations indicate that our hybrid method clearly offers higher profits and lower losses than those of genetic network programming method with rule accumulation (GNP-RA) on all stocks and those o

关键词： Stock trading rule extraction Technical analysis Reinforcement learning Q-learning genetic network programming League championship algorithm

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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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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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Fuzzy Intertransaction Class Association Rule Mining using genetic network programming for Stock Market Prediction

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2011年第4期6卷 353-360页

作者： Yang, Yuchen Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

Intertransaction class association rule (interCAR) has the ability to find the relationships among attributes from different transactions, which has shown its effectiveness for stock market prediction. A crisp interCAR mining method based on genetic network programming (GNP) has been studied in our previous work. But, the crisp method loses much useful information in the discretization and it has many unstable factors influencing the prediction results, so more information is desired in order to make the prediction safer and more efficient. In this paper, a fuzzy interCAR mining method is proposed to keep as much information as possible in the data transformation. Besides, the proposed method has ability that the trading actions bring large profits. The proposed method is applied to Tokyo Stock Exchange, where we compared it with the crisp method as well as some other methods. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： fuzzy intertransaction class association rule mining stock market prediction classification genetic network programming

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Generating Stock Trading Rules Using genetic network programming with Flag Nodes and Adjustment of Importance Indexes

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2011年第1期94卷 25-33页

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

genetic network programming (GNP) is an evolutionary algorithm which represents its solutions by means of graph structures. Since GNP can create quite compact programs and has an implicit memory function, it works especially well in dynamic environments. In addition, a study of the creation of trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been made. The IMX is one of the criteria for decision making. However, the values of the IMXs must be determined by experience and knowledge. Therefore, in this paper, IMXs are adjusted appropriately during stock trading in order to predict the rise and fall of the stocks. In addition, newly defined flag nodes are introduced into GNP, which makes it possible to appropriately judge the current situation of the stock prices, and also contributes to the use of many kinds of nodes in the GNP program. In a simulation, programs were evolved using the stock prices of 20 companies. Then the generalization ability of the method was tested and compared with GNP without flag nodes, GNP without IMX adjustment, and Buy & Hold. (C) 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 94(1): 25-33, 2011;Published online in Wiley Online Library (***). DOI 10.1002/ecj.10269

关键词： evolutionary computation stock trading model genetic network programming flag node technical index

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Autonomous acquisition of cooperative behavior based on a theory of mind using parallel genetic network programming

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ARTIFICIAL LIFE AND ROBOTICS 2011年第2期16卷 157-161页

作者： Minoya, Kenichi Arita, Takaya Omori, Takashi Nagoya Univ Grad Sch Informat Sci Chikusa Ku Nagoya Aichi 4648601 Japan Tamagawa Univ Coll Engn Tokyo Japan

Understanding others as having intentional states, such as beliefs and desires, is called the theory of mind (ToM). To clarify the mechanism of the autonomous acquisition of cooperative behavior based on the ToM, we constructed a functional model of the brain based on the functional parts combination (FPC) model. This model consists of a set of functional parts and activation signals specifying selective activated patterns, and activated modules can be executed in parallel based on the flow of control tokens. The module network and activation signals can be acquired by the evolutionary computation techniques used in genetic network programming and the genetic algorithm, respectively. We used a hunter task as the task to be solved by agents, and encoded inherent activation signals into the genome as a first step. The result of a computer simulation shows the emergence of the pattern of the functional parts for processing the ToM through evolution characterized by punctuated equilibrium.

关键词： Functional model of the brain Theory of mind Cooperation Evolution genetic network programming

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