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genetic network programming with Intron-like Nodes

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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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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genetic network programming with New genetic Operators

Genetic Network Programming with New Genetic Operators

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ye, Fengming Mabu, Shingo Wang, Lutao Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781424465880

Recently, a new approach named genetic network programming (GNP) has been proposed. GNP can evolve itself and find the optimal solutions. It is based on the ideas of classical evolutionary computation methods such as genetic Algorithm (GA) and genetic programming (GP) and uses the data structure of directed graphs which is the unique feature of GNP. Many studies have demonstrated that GNP can well solve the complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, we proposed the new genetic operator named Individual Reconstruction which reconstructs and enhances the worst individuals by using the elite information and the crossover and mutation operators of GNP are also modified. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the new GNP is compared with the conventional GNP. The simulation results show some advantages of the proposed method over the conventional GNPs demonstrating its superiority.

关键词： GNP Individual Reconstruction data mining data mining data structure data structures directed graphs elevator supervised control system evolutionary computation method genetic algorithm genetic algorithms genetic network programming genetic operator stock markets trading rules extraction

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genetic network programming with Automatically Defined Groups for assigning proper roles to multiple agents 05

Genetic Network Programming with Automatically Defined Group...

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genetic and Evolutionary Computation Conference

作者： Murata, Tadahiko Nakamura, Takashi Kansai Univ Fac Informat Takatsuki Osaka 5691095 Japan

ISBN: (纸本)1595930108

In this paper, we apply a genetic network programming (GNP) Architecture using Automatically Defined Groups (ADG) to a multi-agent problem where cooperation of agents are required. GNP is a kind of evolutionary methods inspired from genetic programming (GP). While GP has a tree architecture, GNP has a network architecture with which an agent works in the virtual world. In GNP with ADG, each agent is assigned to a group according to its role to complete some task of a cooperative problem. We consider two types of problems in this paper: one problem is to assign an appropriate role to each agent according to its ability, and the other is to assign a proper role to each agent with the same ability. While the first problem has the specific conditions as for the ability of an agent, the latter is a general problem. We show the effectiveness of GNP with ADG through computer simulations on the two types of load transportation problems.

关键词： algorithms performance genetic network programming cooperation role assignment

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genetic network programming with Route Nodes

Genetic Network Programming with Route Nodes

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IEEE Region 10 Conference on TENCON 2010

作者： Ye, Fengming Mabu, Shingo Wang, Lutao Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

ISBN: (纸本)9781424468904

Many classical methods such as genetic Algorithm (GA), genetic programming (GP), Evolutionary Strategies (ES), etc. have accomplished significant contribution to the study of evolutionary computation. And in the past decade, a new approach named genetic network programming (GNP) has been proposed. It is designed for especially solving complex problems in dynamic environments. Generally speaking, GNP is based on the algorithms of existed classical evolutionary computation techniques and uses the data structure of directed graphs which becomes the unique feature of GNP. So far, many studies have indicated that GNP can solve the complex problems in the dynamic environments very efficiently and effectively. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance of GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated during evolution. And among the accumulated information, some of them are selected and encapsulated in the Route Nodes which are used to guide the evolution process. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the GNP with Route Nodes (GNP-RN) is compared with the conventional GNP. The simulation results show some merits of the proposed method over the conventional GNPs demonstrating its superiority.

关键词： Evolutionary Computation genetic network programming Route Nodes

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genetic network programming with Sarsa Learning Based Nonuniform Mutation

Genetic Network Programming with Sarsa Learning Based Nonuni...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Meng, QingBiao Mabu, Shingo Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781424465880

This paper introduces a nonuniform mutation approach to improve genetic network programming (GNP). GNP is a graph based evolutionary algorithm that has been proven effective on complex optimization problems. Traditionally, GNP maintains its population on the graph level, i.e., the node branches in the same individual are treated uniformly by the genetic operations. It has been observed, however, that even the high-fitness individuals contain logically inappropriate branches, which restricts the evolution of GNP to an extent. In the proposed genetic network programming with Sarsa Learning Based Nonuniform Mutation (GNP-SLNM), we locate the aforementioned undesirable branches by Sarsa learning, and adjust their mutation rates based on the corresponding Q values. The more inappropriate a branch is, the more likely it would be changed. This way, a higher efficiency of the evolution could be achieved. In the experimental studies, we adopt Tileworld problem to compare GNP-SLNM with the conventional GNP, and the results verify GNP-SLNM's superiority in both training and testing phases.

关键词： genetic network programming Sarsa Learning Nonuniform Mutation Evolutionary Computation

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genetic network programming with Generalized Rule Accumulation

Genetic Network Programming with Generalized Rule Accumulati...

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2010 IEEE World Congress on Computational Intelligence

作者： Wang, Lutao Mabu, Shingo Meng, Qingbiao Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424481262

genetic network programming(GNP) is a newly developed evolutionary computation method using a directed graph as its gene structure, which is its unique feature. It is competent for dealing with complex problems in dynamic environments and is now being well studied and applied to many real-world problems such as: elevator supervisory control, stock price prediction, traffic volume forecast and data mining, etc. This paper proposes a new method to accumulate evolutionary experiences and guide agent's actions by extracting and using generalized rules. Each generalized rule is a state-action chain which contains the past information and the current information. These generalized rules are accumulated and updated in the evolutionary period and stored in the rule pool which serves as an experience set for guiding new agent's actions. We designed a two-stage architecture for the proposed method and applied it to the Tile-world problem, which is an excellent benchmark for multi-agent systems. The simulation results demonstrated the efficiency and effectiveness of the proposed method in terms of both generalization ability and average fitness values and showed that the generalized rule accumulation method is especially remarkable when dealing with non-markov problems.

关键词： data mining directed graph directed graphs dynamic environment elevator supervisory control evolutionary computation gene structure generalized rule accumulation genetic algorithms genetic network programming multiagent systems state-action chain stock price prediction tile-world problem traffic volume forecast two-stage architecture

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genetic network programming with class association rule acquisition mechanisms from incomplete database

Genetic Network Programming with class association rule acqu...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Shimada, Kaoru Hirasawa, Kotaro Hu, Jinglu Waseda Univ Informat Prod & Syst Res Ctr Fukuoka Japan Waseda Univ Grad Sch Informat Product & Syst Fukuoka Japan

ISBN: (纸本)9784907764289

A method of class association rule mining from incomplete databases is proposed using genetic network programming (GNP). An incomplete database includes missing data in some tuples, however, the proposed method can extract important rules using these tuples. The proposed mechanisms can calculate measurements of association rules directly using GNP. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. Users can define the conditions of important rules flexibly and obtain enough number of important rules. Generally, it is not easy for Aprior-like methods to extract important rules from incomplete database. We have estimated the performances of the rule extraction and classification of the proposed method using incomplete data set. The results showed that the accuracy of classification of the proposed method is favorable even if some tuples include missing data.

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

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genetic network programming with Parallel Processing for Association Rule Mining in Large and Dense Databases 07

Genetic Network Programming with Parallel Processing for Ass...

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Annual Conference of genetic and Evolutionary Computation Conference

作者： Gonzales, Eloy Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781595936974

Several methods of extracting association rules have been reported. A new evolutionary computation method named genetic network programming (GNP) has also been developed recently and its efectiveness is shown for small datasets. However, it has not been tested for large datasets, particularly in datasets with a large number of attributes. The aim of this paper is to extract association rules from large and dense datasets using GNP considering I real world database with a huge number of attributes. We propose it new method where a large database is divided into many small datasets, then each GNP deals with one dataset having attributes with appropiate size, which was selected randomly from I large dataset and Generated genetically. These GNPs are processed in parallel. We then propose some new genetic operations to improve the number of rules extracted and their quality as well. The proposed method improves remarkably on simulations. Fig. 1 shows the architecture of the proposed method. We use the CLIENT/SERVER model. CLIENT side carries out preprocessing of large database, assignment of files to each server, rule checking, and genetic operations on files. SERVER side carries out processing of each file using. conventional GNP based mining method independently. The features and advantages of the proposed method are the following: Rule extraction is done in parallel. Each file generates its local pool of the rules. Files or datasets are treated as individuals in order to do new genetic operations over them and improve the rule extraction. Extracted rules are stored in a global pool. The rules are verified to avoid redundancy among them and it is assured that only new rules are stored.

关键词： parallel processing association rules genetic network programming

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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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