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No Time Limit and Time Limit Model of Multiple Round Dutch Auction Based on genetic network programming

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第1期15卷 3-12页

作者： Mabu, Shingo Yu, Donggeng Yue, Chuan Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka 8080135 Japan

Nowadays, Dutch auction is used widely at online auction sites. To make online Dutch auction more efficient and more intelligent, it is useful to develop an agent using evolutionary computation which will be adaptive to different auction environments. In this paper, a genetic network programming (GNP) based strategy for auction agents has been proposed to do auctions in multiple round Dutch Auction environments under two types of auction models, no time limit model and time limit model. GNP is a graph-based evolutionary method extended from genetic Algorithms (GA) and genetic programming (GP), which can create optimal solutions by evolution. Although the application of GNP to English auction has been done already, here, a new GNP structure is used for Dutch auction. The simulation results show that the GNP based strategy can also make the agents work well in Dutch auction and the advanced GNP structure makes the agents perform better than that in English auction.

关键词： genetic network programming multiple round dutch auction no time limit model time limit model co-evolution

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Elevator Group Supervisory Control System Using genetic network programming with Functional Localization

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2006年第3期10卷 385-394页

作者： Eguchi, Toru Zhou, Jin Eto, Shinji Hirasawa, Kotaro Hu, Jinglu Markon, Sandor Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Kitakyushu Fukuoka 8030135 Japan Prod Dev HQ Fujitec Co Ltd Osaka Japan

genetic network programming (GNP) having a directed graph structure has been proposed as a new method of evolutionary computation. Recently, GNP has been applied to elevator group supervisory control system (EGSCS), a real-world problem, to demonstrate its applicability and effectiveness. Its previous study considers the known and fixed traffic flow, however, it is changed dynamically with time in real elevator systems. Therefore, an EGSCS with dynamic adaptive control considering such changes should be studied for practical applications. In this paper, we have applied GNP with functional localization to an EGSCS to construct such an adaptive system. In our proposal, the switching GNP can switch the functionally localized GNPs (assigning GNPs) based on the special traffic. Simulation confirmed the adaptability and effectiveness of our proposal in daily office-building traffic.

关键词： elevator group supervisory control system evolutionary computation genetic network programming functional localization real-coded genetic algorithm

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

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2008年第4期12卷 383-392页

作者： Chen, Yan Mabu, Shingo Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka Japan

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 timing of the buying and selling 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 stock trading model technical index candlestick chart

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Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using genetic network programming Algorithms

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ALGORITHMS 2021年第4期14卷 117页

作者： Madokoro, Hirokazu Nix, Stephanie Sato, Kazuhito Iwate Prefectural Univ Fac Software & Informat Sci Takizawa City 0200693 Japan Akita Prefectural Univ Fac Syst Sci & Technol Yurihonjo City 0150055 Japan

This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.

关键词： automatic calibration bed-leaving detection counter-propagation networks genetic network programming piezoelectric films

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genetic network programming with Credit

Genetic Network Programming with Credit

引用

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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Multi-Order Rules Extraction by genetic network programming with Rule Accumulation and its Application to Stock Trading Problems

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第5期15卷 515-524页

作者： Xing, Yafei Mabu, Singo Yuzhu, Lian Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by genetic network programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that the multi-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

关键词： genetic network programming reinforcement learning rule accumulation stock trading multi-order rules

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Discovering fuzzy classification rules using genetic network programming

Discovering fuzzy classification rules using Genetic Network...

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SICE Annual Conference

作者： Karla Taboada Eloy Gonzales Kaoru Shimada Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (纸本)9784907764302

Classification rule mining is an active data mining research area. Most related studies have shown how binary valued datasets are handled. However, datasets in real-world applications, usually consist of fuzzy and quantitative values. As a result, the idea to combine the different approaches with fuzzy set theory has been applied more frequently in recent years. Fuzzy sets can help to overcome the so-called sharp boundary problem by allowing partial memberships to the different sets, not only 1 and 0. On the other hand, fuzzy sets theory has been shown to be a very useful tool because the mined rules are expressed in linguistic terms, which are more natural and understandable for human beings. This paper proposes the combination of fuzzy set theory and ldquogenetic network programmingrdquo (GNP) for discovering fuzzy classification rules from given quantitative data. GNP, as an extension of genetic algorithms (GA) and genetic programming (GP), is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees; this feature contributes creating quite compact programs and implicitly memorizing past action sequences. At last, experimental results conducted on a real world database verify the performance of the proposed method.

关键词： Association rule mining Classification Fuzzy association rules Fuzzy membership functions genetic network programming network programming Fuzzy Association Rules fuzzy membership function Taxonomy Fuzzy Fuzzy Set Theory economic indicators Exploration

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Multi-Car Elevator System using genetic network programming

Multi-Car Elevator System using Genetic Network Programming

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SICE Annual Conference

作者： Lu Yu Jin Zhou S. Mabu K. Shimada K. Hirasawa S. Markon Graduate School ofInformation ProductionandSystems Waseda University Fukuoka Japan Prod.Dev Fujitec Company Limited Hikone Shiga Japan

ISBN: (纸本)9784907764302

Elevator group control systems are the control systems that systematically manage elevators in order to transport passengers efficiently. With the increasing need for high-performance transportation systems in buildings, multi-car elevators where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. genetic network programming(GNP) can introduce various priori knowledge of the elevator systems in its node functions easily and execute an efficient rule-based group control that is optimized evolutionary. This paper discusses the development of controllers for multi-car elevator system (MCES) using GNP The effects for MCES are examined, and we compare the advantages and performances between MCES and double-deck elevator system (DDES).

关键词： Multi-car elevator system genetic network programming Destination floor guidance system Evolutionary computation network programming Elevators evolutionary algorithm elevator shafts control systems Cars

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Trading Rules on Stock Markets Using genetic network programming-Sarsa Learning with Plural Subroutines

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

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SICE Annual Conference

作者： Yunqing Gu Shingo Mabu Yang Yang Jianhua Li Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9784907764388

In this paper, genetic network programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural subroutines. Subroutine node - a new kind of node which works like ADF (Automatically Defined Function) in genetic programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural subroutines with different structures are used to improve the performance of GNP-Sarsa with subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.

关键词： genetic network programming Subroutine Reinforcement learning 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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