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Annual Conference of the SICE

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

ISBN: (纸本)9784907764302

A new evolutionary method named "genetic network programming with control nodes, GNPen" has been applied to determine the timing of buying or selling storks. 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 die 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

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Intertransaction Class Association Rule Mining Based on genetic network programming and Its Application to Stock Market Prediction

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SICE Journal of Control, Measurement, and System Integration 2010年第1期3卷 50-58页

作者： Yuchen Yang Shingo Mabu Kaoru Shimada Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

Intertransaction association rules have been reported to be useful in many fields such as stock market prediction, but still there are not so many efficient methods to dig them out from large data sets. Furthermore, how to use and measure these more complex rules should be considered carefully. In this paper, we propose a new intertransaction class association rule mining method based on genetic network programming (GNP), which has the ability to overcome some shortages of Apriori-like based intertransaction association methods. Moreover, a general classifier model for intertransaction rules is also introduced. In experiments on the real world application of stock market prediction, the method shows its efficiency and ability to obtain good results and can bring more benefits with a suitable classifier considering larger interval span.

关键词： intertransaction class association rule mining genetic network programming classifier model stock market prediction

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An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market

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APPLIED SOFT COMPUTING 2019年 82卷 105551-000页

作者： Ramezanian, Reza Peymanfar, Arsalan Ebrahimi, Seyed Babak KN Toosi Univ Technol Dept Ind Engn Tehran Iran

Evolutionary algorithms are generally used to find or generate the best individuals in a population. Whenever these algorithms are applied to agent systems, they will lead to optimal solutions. genetic network programming (GNP), which contains graph networks, is one of the developed evolutionary algorithms. When the aim is to forecast the share price or return, ascending and descending trends, volatilities, recent returns, fundamental and technical factors have remarkable impacts on the prediction. This is why technical indicators are used to constitute a set of trading rules. In this paper, we apply an integrated framework consisting of GNP model along with a reinforcement learning and Multi-Layer Perceptron (MLP) neural network to classify data and also time series models to forecast the stock return. Moreover, we utilize rules of accumulation based on the GNP model's results to forecast the return. The aim of using these models alongside one another is to estimate one-day return. The results derived from 9 stocks with regard to the Tehran Stock Exchange Market. GNP extracts a prodigious number of rules on the basis of 5 technical indicators with 3 times period. Next, MLP network classifies data and finds the similarity between future data and past data concerning a stock (5 sub-period) through classification. Subsequently, a number of conditions are established, in order to choose the best estimation between GNP-RL and ARMA. Distinct comparison with the ARMA-GARCH model, which is operated for return estimation and risk measurement in many researches, demonstrates an extended forecasting power of the proposed model, by the name of GNP-ARMA, reducing error by a mean of 16%. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Stock return forecasting genetic network programming MLP neural network Technical analysis Classification Time series models Ensemble learning

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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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Graph structure optimization of genetic network programming with ant colony mechanism in deterministic and stochastic environments

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 51卷 100581-000页

作者： Roshanzamir, Mohamad Palhang, Maziar Mirzaei, Abdolreza Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Evolutionary Algorithms are of the most successful algorithms in solving various optimization problems. genetic network programming is one of the Evolutionary Algorithms with good capabilities in agent control problems. In this algorithm, the individuals' structure is a directed graph. Using this structure, it is possible to model the solution of many complex problems. However, in this algorithm, crossover and mutation operators repeatedly break the structures of individuals and make new ones. Although this can lead to better structures, it may break suitable structures in elite individuals. Meanwhile, in stochastic environments, each time an individual is evaluated, it leads to different fitness values. So, calculating the fitness value of individuals requires evaluating each individual several times. This extremely decreases the evolution process speed. In this paper, inspired by mechanisms of ant colony algorithm, a new method is proposed to prevent the algorithm from iteratively breaking down the structures of individuals. This method improves the performance of individuals from one generation to the next using a constructive process. Unlike generative process that the individuals are generated by combination of some others, in constructive process they are produced according to the experience of previous generations. Using this mechanism, we not only prevent breaking suitable structures but also can manage uncertainty in stochastic environments. Our proposed method is used to solve two agent control problems when the environment is deterministic or stochastic. The results show that the proposed algorithm has very high ability in creating an efficient decision making strategies especially in stochastic environments.

关键词： genetic network programming genetic programming Ant colony algorithm Agent control problems Deterministic environment Stochastic environment

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Double-Deck Elevator Group Supervisory Control System Using genetic network programming with Ant Colony Optimization with Evaporation

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2007年第9期11卷 1149-1158页

作者： Yu, Lu Zhou, Jin Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Markon, Sandor Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8030135 Japan Fujitec Co Ltd Prod Dev HQ Hikone Shiga 5228588 Japan

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of genetic Algorithm (GA) and genetic programming (GP), genetic network programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines genetic network programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

关键词： elevator group supervisory control system genetic network programming ant colony optimization hybrid algorithm

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Enhancing Bidding Strategy Using genetic network programming in Agent-Based Multiple Round English Auction

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第7期15卷 747-758页

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

The agent-based auction mechanism widely used in web sites and originally designed for trading goods for customers might not be the most efficient one in the future, while there is a demand of automated auction agents, which are adaptable to the dynamic auction environments. To this end, this paper discusses how to apply genetic network programming (GNP) to automated auction agents in order to make a bid efficiently and effectively at each time step according to the auction environments, and Multiple Round English Auction (MREA) mechanism studied in this paper is based on multi-agent systems, which aims to help the buyer to procure profitable deals as much as possible. GNP-based agent is compared with other agents using conventional strategies in MREA. It has been found from the simulations that the proposed method could help agents to evolve their strategies generation by generation, which shows that GNP has a good performance of helping the agent to find the suitable strategy under various situations and outperform than other strategies.

关键词： genetic network programming bidding strategy agent-based multiple round English auction

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Alternate genetic network programming with Association Rules Acquisition Mechanisms Between Attribute Familiesnn

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2006年第6期10卷 954-963页

作者： Shimada, Kaoru Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

A method of association rule mining with chi-squared test using Alternate genetic network programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.

关键词： evolutionary computation genetic network programming data mining association rules

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A genetic network programming Based Method to Mine Generalized Association Rules with Ontology

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2008年第1期12卷 63-76页

作者： Yang, Guangfei Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

In this paper, we propose a genetic network programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called genetic network programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space.

关键词： generalized association rule genetic network programming ontology dynamic threshold approach

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Fuzzy genetic network programming with Noises for Mobile Robot Navigation

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第7期15卷 767-776页

作者： Sendari, Siti Mabu, Shingo Tjahjadi, Andre Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

genetic network programming (GNP) has been proposed as one of the evolutionary algorithms, which is represented by graph structures. It was extended to GNP with Reinforcement Learning (GNP-RL) which combines online learning and evolution. GNP-RL succeeded in implementing the wall following behaviors of a Khepera robot. The objective of this paper is to improve the robustness of GNP-RL by introducing fuzzy GNP with noises. Fuzzy GNP overcomes the sharp boundary problem using the probabilistic transition on fuzzy judgment nodes, which improves the exploration ability. Furthermore, the robustness of fuzzy GNP can be improved by adding Gaussian noises to the sensors during the training phase. In order to evaluate the robustness of fuzzy GNP with noises, the wall following of a Khepera robot is simulated. Simulation results show that fuzzy GNP with noises is superior to GNP-RL.

关键词： genetic network programming noise reinforcement learning robustness wall following behavior

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