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

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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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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No Time Limit and Time Limit Model of Multiple Round Dutch Auction Based on genetic network programming

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第1期15卷 3-12页

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

Nowadays, Dutch auction is used widely at online auction sites. To make online Dutch auction more efficient and more intelligent, it is useful to develop an agent using evolutionary computation which will be adaptive to different auction environments. In this paper, a genetic network programming (GNP) based strategy for auction agents has been proposed to do auctions in multiple round Dutch Auction environments under two types of auction models, no time limit model and time limit model. GNP is a graph-based evolutionary method extended from genetic Algorithms (GA) and genetic programming (GP), which can create optimal solutions by evolution. Although the application of GNP to English auction has been done already, here, a new GNP structure is used for Dutch auction. The simulation results show that the GNP based strategy can also make the agents work well in Dutch auction and the advanced GNP structure makes the agents perform better than that in English auction.

关键词： genetic network programming multiple round dutch auction no time limit model time limit model co-evolution

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Multi-Order Rules Extraction by genetic network programming with Rule Accumulation and its Application to Stock Trading Problems

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2011年第5期15卷 515-524页

作者： Xing, Yafei Mabu, Singo Yuzhu, Lian Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by genetic network programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that the multi-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

关键词： genetic network programming reinforcement learning rule accumulation stock trading multi-order rules

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Trading Rules on Stock Markets Using genetic network programming-Sarsa Learning with Plural Subroutines

Trading Rules on Stock Markets Using Genetic Network Program...

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SICE Annual Conference

作者： Yunqing Gu Shingo Mabu Yang Yang Jianhua Li Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9784907764388

In this paper, genetic network programming-Sarsa Learning (GNP-Sarsa) used for creating trading rules on stock markets is enhanced by adding plural subroutines. Subroutine node - a new kind of node which works like ADF (Automatically Defined Function) in genetic programming (GP) has been proved to have positive effects on the stock-trading model using GNP-Sarsa. In the proposed method, not only one kind of subroutine but plural subroutines with different structures are used to improve the performance of GNP-Sarsa with subroutines. Each subroutine node could indicate its own input and output node of the subroutine, which could be also evolved. In the simulations, totally 16 brands of stock from 2001 to 2004 are used to investigate the improvement of GNP-Sarsa with plural subroutines. The simulation results show that the proposed approach can obtain more flexible GNP structure and get higher profits in stock markets.

关键词： genetic network programming Subroutine Reinforcement learning Stock trading model Technical index

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A Pruning Method for Accumulated Rules by genetic network programming

A Pruning Method for Accumulated Rules by Genetic Network Pr...

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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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Parallel Evolutionary Association Rule Mining for Efficient Summarization of Wireless Sensor network Data Pattern 5

Parallel Evolutionary Association Rule Mining for Efficient ...

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5th International Conference on Cyber and IT Service Management (CITSM)

作者： Wedashwara, Wirarama Mabu, Shingo Ahmadi, Candra STIKOM Bali Bali Indonesia Yamaguchi Univ Ube Yamaguchi Japan

ISBN: (纸本)9781538627372

Evolutionary computation is widely used to solve dynamic problems such as association rule mining(ARM) by adapting the solutions to changes of the data pattern. The summarization in the ARM for a wireless sensor network(WSN) is still an issue when its applied to big number of sensor input from multiple location. This paper proposes a parallel processing of ARM for efficient WSN processing using genetic network programming (GNP). The proposed method adopt the concept of "assumption" as rules from the training data and optimize the rules definition to be a "specific rules" for each sensors network location via parallel evolutionary processing. Then summarization is build by calculating hierarchy of "common rules" or similarity between different location. The simulation done using the set of weather forecast sensors. The results shows that the proposed method is capable to efficiently summarize the sensor input from multiple location with online processing and archived a close result to conventional method that performed without on-line processing.

关键词： Evolutionary Computation genetic network programming Rule Association Mining Wireless Sensor network Internet of Things

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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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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 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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