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

作者： Yue, Chuan Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781457706530

On-line auctions are the electronic marketplaces for trading commodities or services in modern life. Continuous Double Auction(CDA), which is different from the single-side auction types, allows multiple sellers and multiple buyers to update their asks and bids at the same time successively through the trading period. This paper discusses a new proposed CDA bidding strategy based on genetic network programming(GNP) with generalized judgments using Gaussian functions for the agent-based CDA environment. GNP is one of the evolutionary computations, and the GNP-based bidding strategy can judge and analyze various situations of the ongoing auction using its judgment functions, then determine the most competitive and suitable ask or bid price at each time step according to the directed graph structure of the GNP individual. Especially, in the proposed GNP-based method, the generalized judgment functions are proposed using. m-dimensional Gaussian Functions with evolution instead of conventional fixed judgment functions in order to judge the dynamically changing auction situations more sensitively and comprehensively. In addition, the proposed method uses some basic heuristic control rules for helping the auction agent to make ask or bid decisions. The performance of the proposed bidding strategy are studied and compared with other methods in CDA under different settings.

关键词： genetic network programming Continuous Double Auctions Asking and Bidding Strategy Generalized Judgment Nodes

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Automatic Calibration of Bed-Leaving Sensor Signals Based on genetic Evolutionary Learning 18

Automatic Calibration of Bed-Leaving Sensor Signals Based on...

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18th International Conference on Control, Automation and Systems (ICCAS)

作者： Hiramatsu, Daiju Madokoro, Hirokazu Sato, Kazuhito Nakasho, Kazuhisa Shimoi, Nobuhiro Akita Prefectural Univ Fac Syst Sci & Technol Dept Machine Intelligence & Syst Engn Yurihonjo City Akita 0150055 Japan Yamaguchi Univ Fac Engn Div Informat Sci & Engn Ube Yamaguchi 7558611 Japan

ISBN: (纸本)9788993215151

This paper presents a method to generate filters for shaping sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. In our previous 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). The system can learn topology and relations between input features and teaching signals. However, our method based on CPNs was 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 obtained using sensor signals obtained in our previous study. 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 the difference between original signal patterns and ideal signal patterns. For the application experiment, we used fitness calculated from the recognition accuracy of CPNs. The experimentally obtained results reveal that the mean accuracy improved 6.53 percentage point for three datasets.

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

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Fuzzy genetic network programming with Reinforcement Learning for Mobile Robot Navigation

Fuzzy Genetic Network Programming with Reinforcement Learnin...

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

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

ISBN: (纸本)9781457706530

This paper proposes Fuzzy genetic network programming with Reinforcement Learning (Fuzzy GNP-RL). This method integrates fuzzy logic to the conventional GNP-RL. The new part of the proposed method is fuzzy judgment nodes. Fuzzy GNP-RL provides flexibility to determine the appropriate next node by the probabilistic transition instead of that by the threshold values on GNP-RL. The simulation of the wall following behavior of a Khepera robot is used to evaluate the performance of Fuzzy GNP-RL compared with that of GNP-RL. The result shows that Fuzzy GNP-RL is more robust than GNP-RL.

关键词： Fuzzy logic genetic network programming Reinforcement Learning Robustness Wall following behavior

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Enhanced Rule Extraction and Classification Mechanism of genetic network programming for Stock Trading Signal Generation 11

Enhanced Rule Extraction and Classification Mechanism of Gen...

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13th Annual genetic and Evolutionary Computation Conference (GECCO)

作者： Mabu, Shingo Hirasawa, Kotaro Waseda Univ Tokyo Japan

ISBN: (纸本)9781450305570

Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. genetic network programming (GNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model.

关键词： evolutionary computation rule extraction genetic network programming stock trading technical analysis

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genetic network programming-Sarsa with Subroutines for Trading Rules on Stock Markets

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第5期15卷 488-494页

作者： Yang, Yang Mabu, Shingo Li, Jianhua Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

The purpose of this paper is to propose a new approach to generate effective subroutines, which are automatically discovered by the GNP-Sarsa programs combining evolution and reinforcement learning. We name it asGNP-Sarsa with subroutines (GNP(sb)-Sarsa) and apply it to the trading rules on stock markets. In the proposal method, GNP(sb)-Sarsa offers an alternative population, where individuals are represented by subroutines. Each main program of GNP(sb)-Sarsa can refer to an individual in the subroutine population, after adding a new kind of node, namely subroutine node, in the graph network structure of GNP-Sarsa. GNP(sb)-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNP(sb)-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNP(sb)-Sarsa are constrained by the gene structure on which they can operate. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness of the GNP(sb)-Sarsa. The results show that the proposed approach can provide reasonable opportunities for evolving complex solutions.

关键词： genetic network programming subroutine evolutionary algorithms stock trading model technical index

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Variable Size genetic network programming with Binomial Distribution

Variable Size Genetic Network Programming with Binomial Dist...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Li, Bing Li, Xianneng Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781424478354

This paper proposes a different type of genetic network programming (GNP) - Variable Size genetic network programming (GNPvs) with Binomial Distribution. In contrast to the individuals with fixed size in Standard GNP, GNPvs will change the size of the individuals and obtain the optimal size of them during evolution. The proposed method defines a new type of crossover to implement the new feature of GNP. The new crossover will select the number of nodes to move from each parent GNP to another parent GNP. The probability of selecting the number of nodes to move satisfies the binomial probability distribution. The proposed method can keep the effectiveness of crossover and improve the performance of GNP. In order to verify the performance of the proposed method, a well-known benchmark problem - Tile-world is used in the simulations. The simulation results show the effectiveness of the proposed method.

关键词： genetic network programming Variable size Crossover Binomial probability distribution Tile-world

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

Autonomous acquisition of cooperative behavior based on a th...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Minoya, Kenichi Arita, Takaya Omori, Takashi Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan Tamagawa Univ Coll Engn Machida Tokyo 1948610 Japan

ISBN: (纸本)9784990288051

Understanding of others as having intentional states such as beliefs and desires is called 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 the genetic network programming and genetic Algorithm, respectively. We use a hunter task as a task to be solved by the agents, and encode inherent activation signals into the genome as a first step. The result of computer simulation shows an emergence of the pattern of the functional parts for processing 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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Improving the robustness of GNP-PCA using the multiagent system

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APPLIED SOFT COMPUTING 2017年 61卷 757-764页

作者： Yu, Shanqing Zhang, Deng Mabu, Shingo Chen, Jinyin Hirasawa, Kotaro Zhejiang Univ Technol Coll Informat Engn 288 Liuhe Rd Hangzhou Zhejiang Peoples R China Hangzhou ChinaOly Tech Co Ltd Inst Zhejiang ChinaOly Police Cloud Informat 9Jiusheng Rd Hangzhou Zhejiang Peoples R China Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka Japan

In order to improve the robustness of genetic network programming fuzzy data mining and PCA (GNP-PCA) based face recognition in the Gaussian and Salt&Pepper noisy testing environments, a GNP-based multi-agent system is constructed using GNP-PCA and multi-resolution analysis in this paper. In the proposed approach, the different scales of training images in the Laplacian pyramid are regarded as sub-environments and each GNP-PCA is performed as an agent in its corresponding environment. Face recognition is finally realized by maximizing the weighted average matching degrees of all the persons in the training database. Experimental results indicate that the proposed method has improved the robustness of GNP-PCA in the Gaussian and Salt&Pepper noisy testing environments considerably. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Face recognition Gaussian pyramid genetic network programming Laplacian pyramid Multiagent system Principal Component Analysis Robustness

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Multicar Elevator Group Supervisory Control System using genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2011年第S1期6卷 S65-S73页

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

Elevator group control systems are the transportation systems for handling passengers in the buildings. With the increasing demand for high-rise buildings, it becomes important to improve the elevator service. The multicar elevators consist of plural cars in a single elevator shaft. It contributes to the improvement in passengers' handling capacity, while allowing the reduction of the space occupied in the building. In contrast with traditional elevator systems, the cars can no longer operate freely, where there are several restrictions on their available movements. This requires a more difficult control including stochastic scheduling with high combinatorial complexity in order to make the system more flexible. At present, lots of buildings with more than 40 floors are being built, which are usually divided into several zones served by local elevator groups. In addition, the cars should be operated at equal time intervals, especially in such a building with multicar elevator systems (MCES) in order to obtain its good performance. genetic network programming (GNP), one of the evolutionary computations, can realize a rule-based MCES due to its directed graph structure of the individual, which makes the system more flexible. This paper discusses MCES using GNP in high-rise buildings. Also, the positions of elevators are considered to avoid the bunching phenomenon. The performance of MCES is studied and compared with single-deck elevator system (SDES) and double-deck elevator system (DDES). (C) 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： multicar elevator system genetic network programming high-rise building bunching

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genetic network programming with Intron-like Nodes

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2010年第8期93卷 23-31页

作者： Mabu, Shingo Chen, Yan Eto, Shinji Shimada, Kaoru Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan Waseda Univ Informat Prod & Syst Res Ctr Tokyo Japan

Recently, genetic network programming (GNP), an extension of the genetic algorithm (GA) and genetic programming (GP), has been proposed. GNP can make compact programs and can memorize the past history implicitly, because it expresses the solution by directed graphs and therefore can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that intron-like nodes are useful for improving the training speed and generalization ability. (C) 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(8): 23-31, 2010;Published online in Wiley Inter Science (***). DOI 10.1002/ecj.10259

关键词： evolutionary computation genetic network programming directed graph intron tile world

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