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SICE Annual Conference

作者： Lutao Wang Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9784907764388

genetic network programming(GNP) is a newly developed evolutionary computation method. A GNP based rule accumulation method(GNP-RA) is also proposed to generate decision rules and accumulate them into the rule pool, which serves as an experience set for agent control problems. Elite individuals are regarded as evolving rule generators and the extracted rules are viewed as solutions, which is different from the conventional evolutionary computation methods. However, even the best individual could generate some bad rules, thus the interesting rules and uninteresting rules are hard to distinguish. This paper proposed a method to prune the uninteresting rules in the rule pool so that the interesting ones could stand out, which helps to increase the accuracy of decision making. The efficiency and effectiveness of the proposed method is verified by the tile-world problem, which is an excellent benchmark in multi-agent systems.

关键词： genetic network programming Rule Accumulation Rule Pruning Multi-agent System Tile-world

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Evaluation of varying portfolio construction of stocks using genetic network programming with control nodes

Evaluation of varying portfolio construction of stocks using...

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SICE Annual Conference

作者： Etsushi Ohkawa Yan Chen Shingo Mabu Kaoru Shimada Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Japan

ISBN: (纸本)9784907764302

A new evolutionary method named ldquogenetic network programming with control nodes, GNPcnrdquo has been applied to determine the timing of buying or selling stocks. GNPcn represents its solutions as directed graph structures which has some useful features inherently. For example, GNPcn has an 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 determine the strategy of buying and selling stocks of multi issues. And GNPcn can distribute the purchase capital to each stock based on the distribution ratio. The effectiveness of the proposed method is confirmed by simulations.

关键词： Evolutionary computation genetic network programming Candlestick Chart Buying and selling stocks network programming evolutionary algorithm stocks network flow Ganglia Purchasing Memory buying and selling

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Analysis of Crossover Rate in Variable Size genetic network programming with Binomial Distribution

Analysis of Crossover Rate in Variable Size Genetic Network ...

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SICE Annual Conference

作者： Bing Li Xianneng Li Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9784907764388

Variable Size genetic network programming (GNPvs) with Binomial Distribution is a new type of genetic network programming, which permits the individuals with variable size and obtains the optimal size of individuals during evolution. In contrast to swapping the corresponding nodes in the crossover of genetic network programming (GNP), the crossover of GNPvs moves a number of nodes from each parent individual to another parent individual. The probability of selecting the number of nodes to move satisfies the binomial distribution, which is controls by the crossover rate and the initial size of the individuals. In this paper, the performance of the algorithm and the size of the individuals are studied by changing the most important parameter, i.e., crossover rate. Since GNPvs may suffer from the bloating problem, the Island Model is introduced to control the size of the individuals implicitly. The well-known test bed Tileworld is used to show the numeric results in the simulations.

关键词： genetic network programming Variable size Crossover Binomial distribution Bloating problem

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Idle cage assignment algorithm-embedded controller of Dould-Deck Elevator Systems using genetic network programming

Idle cage assignment algorithm-embedded controller of Dould-...

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SICE Annual Conference

作者： Jin Zhou Lu Yu Shingo Mabu Kaoru Shimada Kotaro Hirasawa Sandor Markon Graduate School ofInformation Production and Systems Waseda University Wakamatsu Japan Prod.Dev.HQ g Wing Fujitec Company Limited Hikone Shiga Japan

ISBN: (纸本)9784907764302

So far, many studies on Double-Deck elevator systems (DDES) have been done for exploring some more efficient algorithms to improve the system transportation capacity, especially in a heavy traffic mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. Unlike what happens in this mode, where all cages almost always keep moving, there is the case, where some cages become idle in a light traffic mode. Therefore, how to dispatch these idle cages, which is seldom considered in the heavy traffic mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller using genetic network programming with idle cage assignment algorithm embedded for a light traffic mode.

关键词： Double-Deck Elevator Systems Evolutionary Computation genetic network programming network programming traffic patterns evolutionary algorithm cages Controllers Elevators round trips

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A Learning Classifier System Based on genetic network programming

A Learning Classifier System Based on Genetic Network Progra...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Xianneng Li Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Japan

ISBN: (纸本)9781479906505

Recent advances in Learning Classifier Systems (LCSs) have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based genetic network programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the decision-making rules. XrGNP is described in details in which its unique features are explicitly mapped. Experiments on benchmark and real-world multi-step problems demonstrate the effectiveness of XrGNP.

关键词： learning classifier systems genetic network programming niching fitness sharing reinforcement learning network programming learning (artificial intelligence) Learning Classifiers niches learning systems

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A Fast Evolutionary Learning to Optimize CNN

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Chinese Journal of Electronics 2020年第6期29卷 1061-1073页

作者： CHEN Jinyin LIN Xiang GAO Shangtengda XIONG Hui ZHANG Longyuan LIU Yi XUAN Qi College of Information Engineering Zhejiang University of Technology NESA Lab Zhejiang University

Deep neural networks(DNNs) show great performance in lots of *** neural network(CNN) is one of the classic DNNs,and various modified CNNs have been brought up,such as DenseNet,GoogleNet,ResNet,*** diverse tasks,a unique structure of CNN may show its ***,how to design an effective CNN model for a practical task is a *** this paper,we model the architecture optimization of CNN as an optimization problem and design a genetic network programming based Fast evolutionary learning(GNP-FEL) to optimize ***-FEL contains three main ideas:First GNP is adopted to optimize CNN architecture and hyperparameters,which can build diverse network structures and make network parameters selfevolve;Second multi-objective optimization is designed by balancing both CNN model efficiency and structure compactness;Last a novel incremental training method is proposed to train offspring CNN models in GNP,which is capable of reducing time complexity *** have validated that GNP-FEL can quickly evolve a CNN classifier with a sufficiently compact *** the classifier has a comparable classification effect to state-ofthe-art CNN model.

关键词： convolutional neural nets genetic algorithms learning (artificial intelligence) neural net architecture optimize CNN deep neural networks convolutional neural network DNN diverse tasks CNN model practical task architecture optimization optimization problem genetic network programming GNP-FEL diverse network structures network parameters multiobjective optimization structure compactness offspring CNN models CNN classifier state-ofthe-art CNN model fast evolutionary learning Evolutionary learning genetic network programming (GNP) CNN Multi-objective optimization Incremental training

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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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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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GNPA: a hybrid model for social influence maximization in dynamic networks

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期83卷 3057-3084页

作者： Agarwal, Sakshi Mehta, Shikha Jaypee Inst Informat Technol Comp Sci & Informat Technol Noida 201309 India

With the growing size of online social communities, influence propagation in social networks has become a hot topic of the research. Most of the studies in this area are based on the assumption that the structure of the social network is static and does not change during the information spread process. However, real-world social networks are dynamic. Modeling this continuous dynamic behavior of social networks is a challenge that must be tackled. This paper proposes a hybrid genetic network programming with Apriori algorithm (GNPA) for Influence Maximization (IM) in social networks. Proposed GNPA is a meta-heuristic based optimization algorithm that handles the dynamicity, i.e. user attribute values or connection between users that changes with time. The working of GNPA is divided into 5 steps. It begins with the identification of the initial population of seeds using discounted degree method. Next, it predicts the future changes using the Apriori algorithm and updates the network accordingly. After updating the network dynamics, the influence score is estimated using the local consistent Factorization machines for each edge of the network. Finally, the diffusion score of each individual of the population is calculated using a linear cascade model. After the completion of all the above steps, the population is updated by replacing the weak individuals with new individuals using mutation and crossover, which is the last step of GNPA. The efficacy of GNPA is evaluated over two real and two synthetic datasets with low out-degree ratio and high out-degree ratio. Experimental results demonstrated that GNPA is able to predict the changing behavior of the users close to the actual-time network and improved the influence propagation 16% to 38% as compared to the contemporary counterparts.

关键词： Social network Influence maximization genetic algorithm genetic network programming Apriori algorithm Graph theory

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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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