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检索条件"主题词=Genetic Network Programming"

共 139 条记录，以下是51-60 订阅

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Double-Deck Elevator Systems with Idle Cage Assignment using genetic network programming

Double-Deck Elevator Systems with Idle Cage Assignment using...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yu, Lu Mabu, Shingo Zhou, Jin Eto, Shinji Hirasawa, Kataro Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Fukuoka 8080135 Japan

ISBN: (纸本)9781424465880

Many studies on Double-Deck Elevator Systems (DDES) have been done for exploring 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 occurs 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 with idle cage assignment algorithm using genetic network programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the efficiency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern. Simulation results show that the proposed method improves the performance comparing to the case when the cage assignment algorithm is not employed and works better than six other heuristic methods in a light traffic mode.

关键词： Double-Deck Elevator Systems Evolutionary Computation genetic network programming

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Usage of Frequently Used Node in Variable Size genetic network programming

Usage of Frequently Used Node in Variable Size Genetic Netwo...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Li, Bing Yu, Shanqing Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781479906529

This paper describes a kind of replacement mechanism which improves the generalization ability of Variable Size genetic network programming (GNPvs). GNPvs is an extension of genetic network programming (GNP), which has changeable number of nodes. Inspired by the theory of Evolution by Gene Duplication, the non-frequently used nodes are replaced with the frequently used nodes in the proposed method, which can make the individual survive under the selection pressure as usual, but eventually might accumulate mutations that produce new features of individuals for adapting new environments. The effectiveness of the proposed method is verified by comparing with the performance of GNPvs and GNP on a well-known dynamic multi-agent test bed - Tileworld.

关键词： genetic network programming Variable size Generalization Tile-world

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Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions

Association rules mining for handling continuous attributes ...

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

作者： Taboada, Karla Shimada, Kaoru Mabu, Shingo Hirasawa, Kotaro Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Kitakyushu Fukuoka 8080135 Japan

ISBN: (纸本)9784907764289

Data Mining is the process of extracting useful hidden knowledge from large volumes of data and its results can be used in decision support systems. Several data mining algorithms have been developed;one example is the association rule mining, which discovers associations among items encoded within a database. When the values of the attributes in a database are continuous such as height, length or weight, their domain is usually discretized into several intervals, as a result, such attributes are handled as discrete attributes. In this study, a new approach of mining association rules for handling continuous attributes that does not use any discretization is proposed. The methodology is based on a new graph-based evolutionary algorithm named "genetic network programming (GNP)" and fuzzy membership functions. Our data mining method first needs to transform the continuous values in transactions into linguistic terms, then judge them and find association rules using GNP. GNP represent its individuals using graph structures that evolve in order to find a solution;this feature contributes to creating quite compact programs and implicitly memorizing past action sequences. The proposed method can measure the significance of the extracted association rules by using support, confidence and chi(2) test, and obtains a sufficient number of important association rules in a short time.

关键词： data mining association rules fuzzy membership functions genetic network programming

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Tasks Decomposition for Improvement of genetic network programming 9

Tasks Decomposition for Improvement of Genetic Network Progr...

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9th International Conference on Computer and Knowledge Engineering (ICCKE)

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

ISBN: (纸本)9781728150758

genetic network programming is an evolutionary algorithm which can be considered as an extension of genetic programming but with a graph-structure instead of tree-structure individuals. This algorithm is mainly used for single/multi-agent decision making. It uses a graph to model a strategy that an agent follows to achieve its goal. However, in this algorithm, crossover and mutation operators repeatedly destroy the structures of individuals and make new ones. Although this can lead to better structures, it may also break suitable structures in elite individuals and increase the time needed to achieve optimal solutions. In this research, we modified the evolution process of genetic network programming so that breaking useful structures will be less likely. In the proposed algorithm, the experiences of the best individuals in successive generations are saved. Then, in some specific generations, these experiences are used to generate offspring. The experimental results of the proposed method were tested on two common agent control problem benchmarks namely Tile-world and Pursuit-domain. The results showed the superiority of our method with respect to standard genetic network programming and some of its versions.

关键词： Evolutionary algorithms genetic programming genetic network programming Graph structure Agent control problems

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A Bidding Strategy of Multiple Round Auctions based on genetic network programming

A Bidding Strategy of Multiple Round Auctions based on Genet...

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2010 IEEE World Congress on Computational Intelligence

作者： Yue, Chuan Mabu, Shingo Yu, Donggeng Wang, Yu Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan

ISBN: (纸本)9781424481262

Recently, due to the development of the e-commerce, on-line auctions have become a common modern way to trade goods over web. In order to make the trade more efficient and more intelligent, a new strategy has been proposed for bid agents to participate in multiple round auctions to deal with multiple goods. The proposed strategy is developed based on evolutionary genetic network programming (GNP), which uses directed graph structures for getting the optimal solution. In this paper, we considered the most popular two kinds of auctions, English auction and Dutch auction. It is found from the simulation results that the agents adopting the GNP-based strategy performed very well in terms of getting more goods with less money, and they can make their decisions dynamically in response to the changes of the multiple round environments both in English auction and Dutch auction.

关键词： GNP bidding strategy directed graph structures e-commerce electronic commerce genetic algorithms genetic network programming graph theory multiple round auctions optimal solution

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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 (SMC)

作者： Li, Xianneng Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781479906529

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

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A Stock Trading Model for Multi-Brands Optimization Based on genetic network programming with Control Nodes

A Stock Trading Model for Multi-Brands Optimization Based on...

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Annual Conference of the SICE

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

ISBN: (纸本)9784907764302

Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named "genetic network programming" (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a Multi-Brands optimization system based on genetic network programming with control nodes is presented. This method makes use of the information from Technical Indices and Candlestick Chart. The proposed optimization system, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency.

关键词： Multi-Brands Optimization genetic network programming Control Node Reinforcement Learning

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Probabilistic Model Building genetic network programming Using Multiple Probability Vectors

Probabilistic Model Building Genetic Network Programming Usi...

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IEEE Region 10 Conference on TENCON 2010

作者： Li, Xianneng Mabu, Shingo Mainali, Manoj K. Hirasawa, Kotaro Waseda Univ Grad Sch Informat Prod & Syst Tokyo Japan

ISBN: (纸本)9781424468904

As an extension of GA and GP, a new evolutionary algorithm named genetic network programming (GNP) has been proposed. GNP uses the directed graph structure to represent its solutions, which can express the dynamic environment efficiently. The reusable nodes of GNP can construct compact structures, leading to a good performance in complex problems. In addition, a probabilistic model building GNP named GNP with Estimation of Distribution Algorithm (GNP-EDA) has been proposed to improve the evolution efficiency. GNP-EDA outperforms the conventional GNP by constructing a probabilistic model by estimating the probability distribution from the selected elite individuals of the previous generation. In this paper, a probabilistic model building GNP with multiple probability vectors (PMBGNP(M)) is proposed. In the proposed algorithm, multiple probability vectors are used in order to escape from premature convergence, and genetic operations like crossover and mutation are carried out to the probability vectors to maintain the diversities of the populations. The proposed algorithm is applied to the controller of autonomous robots and its performance is evaluated.

关键词： autonomous robot controller convergence directed graph directed graphs distribution algorithm evolutionary algorithm genetic algorithms genetic network programming mobile robots multiple probability vector probabilistic logic probabilistic model statistic

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A Study on Energy Consumption of Elevator Group Supervisory Control Systems using genetic network programming

A Study on Energy Consumption of Elevator Group Supervisory ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yu, Lu Mabu, Shingo Zhang, Tiantian Hirasawa, Kotaro Ueno, Tsuyoshi Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku Hibikino 2-7 Fukuoka 8080135 Japan Cent Res Inst Elect Power Ind Komae Tokyo 2018511 Japan

ISBN: (纸本)9781424427932

Elevator group supervisory control system (EGSCS) is a traffic system, where its controller manages the elevator movement to transport passengers in buildings efficiently. Recently, Artificial Intelligence (AI) technology has been used in such complex systems. genetic network programming(GNP), a graph-based evolutionary method extended from GA and GP, has been already applied to EGSCS. On the other hand, since energy consumption is becoming one of the greatest challenges in the society, it should be taken as criteria of the elevator operations. Moreover, the elevator with maximum energy efficiency is therefore required. Finally, the simulations show that the elevator system has the higher energy consumption in the light traffic, thus, some factors have been introduced into GNP for energy saving in this paper.

关键词： Elevator Group Supervisory Control System Energy Consumption genetic network programming

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Improving Coordination via Emergent Communication in Cooperative Multiagent Systems: A genetic network programming Approach

Improving Coordination via Emergent Communication in Coopera...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Naeini, Armin Tavakoli Ghaziasgar, Mehran Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Islamic Azad Univ Dept Comp Sci Esfahan Iran

ISBN: (纸本)9781424427932

The design and development of strategies to coordinate the actions of multiple agents is a central research issue in the field of multiagent systems. To address this issue, in our previously reported research we proposed a novel methodology based on genetic network programming (GNP) to allow agents in the pursuit domain to autonomously learn an effective coordination strategy in order to achieve group behavior. GNP is a newly developed Evolutionary Computation (EC) technique whose genome is network structure. In this paper, we extend our methodology by allowing agents in the pursuit domain to autonomously learn communication. We consider autonomous and independently learning agents, and we seek to obtain an optimal solution for the team as a whole. We design a novel methodology for the emergence of communication between agents in cooperative multiagent systems based on GNP and we seek to obtain more coordination. Through simulations, we demonstrate that the proposed approach is effective in evolving communicating agents and furthermore a comparison is made between agents with and without communication in order to show that the emergent communication among agents is beneficial Le., improves their coordination. In addition, we show the robustness of generated programs which is achieved as a side-effect of the capability of communication.

关键词： Evolutionary Computation Communication Coordination genetic network programming Multiagent Systems

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