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

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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genetic network programming with subroutines for automatic program generation

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第2期7卷 197-207页

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

In this paper, a subroutine mechanism is introduced in genetic network programming for automatic program generation (GNP-APG). The proposed method named GNP with subroutines for APG (GNPsr-APG) is an extension of the algorithm of GNP-APG, where hierarchy programs are generated: in other words, programs that contain a main function and subroutines are generated. The proposed method automatically defines the main function and use of the potentially useful subroutines during evolution. By using subroutines, a complex program can be decomposed to several simple programs which are obtained more easily. Moreover, these subroutines are called many times from a main program, which results in reducing the size of the program significantly. The simulation results verify that the proposed method can improve the performance of GNP-APG and reduce the size of the program. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： genetic network programming genetic programming program generation subroutines Tile-world

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Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

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ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA 2022年第1期35卷 5971-5996页

作者： Chen, Yan Mo, Dongxu Zhang, Feipeng Hunan Univ Business Sch Hunan Key Lab Data Sci & Blockchain Changsha Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Xi An Jiao Tong Univ Sch Finance & Econ Xian Peoples R China

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction.

关键词： Evolutionary computations decision analysis association rule mining genetic network programming stock movement prediction

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Agent-based artificial financial market with evolutionary algorithm

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ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA 2022年第1期35卷 5037-5057页

作者： Chen, Yan Xu, Zezhou Yu, Wenqiang Hunan Univ Business Sch Hunan Key Lab Data Sci & Blockchain Changsha Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China

In traditional financial studies, existing approaches are unable to address increasingly complex problems. In this paper, an artificial financial market is proposed, in accordance with the adaptation market hypothesis, using artificial intelligence algorithms. This market includes three types of agents with different investments and risk preferences, representing the heterogeneity of traders. genetic network programming is combined with a state-action-reward-state-action (SARSA)(lambda) algorithm for designing the market to reflect the adaptation of technical agents. A pricing mechanism is taken into consideration, based on the auction mechanism of the Chinese securities market. The characteristics of price time series are analyzed to determine whether excessive volatility exists in four different markets. Explanations are provided for the corresponding financial phenomena considering the hypotheses under the proposed novel artificial financial market.

关键词： Artificial financial market evolutionary algorithm genetic network programming SARSA(lambda) algorithm

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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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genetic network programming with Credit

Genetic Network Programming with Credit

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Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE)

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

ISBN: (纸本)9784907764401

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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Evolving asset selection using genetic network programming

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2012年第2期7卷 174-182页

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

As global financial innovation opens innumerable risks and opportunities, a global view of the asset allocation brings advantages in risk diversification for investments. We propose a novel framework for asset selection under global diversification principles using genetic network programming. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and offers competitive advantages against the conventional methods in finance and computational fields. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： genetic network programming asset selection value and growth evolutionary finance

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Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第16期39卷 12349-12357页

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

genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Evolutionary computation genetic network programming Reinforcement learning Adaptability Khepera robot

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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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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 (SMC)

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

ISBN: (纸本)9781467317146

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, 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

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