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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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Evaluation of varying portfolio construction of stocks using Genetic network programming with control nodes

Evaluation of varying portfolio construction of stocks using...

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SICE Annual Conference

作者： Etsushi Ohkawa Yan Chen Shingo Mabu Kaoru Shimada Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Japan

ISBN: (纸本)9784907764302

A new evolutionary method named ldquogenetic network programming with control nodes, GNPcnrdquo has been applied to determine the timing of buying or selling stocks. 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 the 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 network programming evolutionary algorithm stocks network flow Ganglia Purchasing Memory buying and selling

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Discovering fuzzy classification rules using Genetic network programming

Discovering fuzzy classification rules using Genetic Network...

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SICE Annual Conference

作者： Karla Taboada Eloy Gonzales Kaoru Shimada Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (纸本)9784907764302

Classification rule mining is an active data mining research area. Most related studies have shown how binary valued datasets are handled. However, datasets in real-world applications, usually consist of fuzzy and quantitative values. As a result, the idea to combine the different approaches with fuzzy set theory has been applied more frequently in recent years. Fuzzy sets can help to overcome the so-called sharp boundary problem by allowing partial memberships to the different sets, not only 1 and 0. On the other hand, fuzzy sets theory has been shown to be a very useful tool because the mined rules are expressed in linguistic terms, which are more natural and understandable for human beings. This paper proposes the combination of fuzzy set theory and ldquogenetic network programmingrdquo (GNP) for discovering fuzzy classification rules from given quantitative data. GNP, as an extension of genetic algorithms (GA) and genetic programming (GP), is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees; this feature contributes creating quite compact programs and implicitly memorizing past action sequences. At last, experimental results conducted on a real world database verify the performance of the proposed method.

关键词： Association rule mining Classification Fuzzy association rules Fuzzy membership functions Genetic network programming network programming Fuzzy Association Rules fuzzy membership function Taxonomy Fuzzy Fuzzy Set Theory economic indicators Exploration

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Idle cage assignment algorithm-embedded controller of Dould-Deck Elevator Systems using genetic network programming

Idle cage assignment algorithm-embedded controller of Dould-...

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SICE Annual Conference

作者： Jin Zhou Lu Yu Shingo Mabu Kaoru Shimada Kotaro Hirasawa Sandor Markon Graduate School ofInformation Production and Systems Waseda University Wakamatsu Japan Prod.Dev.HQ g Wing Fujitec Company Limited Hikone Shiga Japan

ISBN: (纸本)9784907764302

So far, many studies on Double-Deck elevator systems (DDES) have been done for exploring some 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 happens 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 using genetic network programming with idle cage assignment algorithm embedded for a light traffic mode.

关键词： Double-Deck Elevator Systems Evolutionary Computation Genetic network programming network programming traffic patterns evolutionary algorithm cages Controllers Elevators round trips

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Research on Teaching Reform and Innovation of the Course of Computer network programming

Research on Teaching Reform and Innovation of the Course of ...

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2017 International Conference on Advanced Education,Psychology and Sports Science(AEPSS 2017)

作者： Jinzhu Huang Meilan Huang Guangxi Technological College of Machinery and Electricity

With the development of computer network, the computer network programming has become an important technology. Computer network programming course has received widespread attention in the colleges of China. However, there are some serious problems in the teaching of computer network programming, such as the backward teaching idea, single teaching model and outdated teaching contents. In view of these problems, this paper puts forward the corresponding countermeasures to provide some references for the relevant researchers.

关键词： Teaching reform and innovation network programming programming design

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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

作者： Chuan Yue Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781467317139

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, and 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 adjustable parameter network programming Intelligent agents economic indicators Congenital Dyserythropoietic Anemia large sizes market mechanism

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Multi-Car Elevator System using Genetic network programming

Multi-Car Elevator System using Genetic Network Programming

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SICE Annual Conference

作者： Lu Yu Jin Zhou S. Mabu K. Shimada K. Hirasawa S. Markon Graduate School ofInformation ProductionandSystems Waseda University Fukuoka Japan Prod.Dev Fujitec Company Limited Hikone Shiga Japan

ISBN: (纸本)9784907764302

Elevator group control systems are the control systems that systematically manage elevators in order to transport passengers efficiently. With the increasing need for high-performance transportation systems in buildings, multi-car elevators where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. Genetic network programming(GNP) can introduce various priori knowledge of the elevator systems in its node functions easily and execute an efficient rule-based group control that is optimized evolutionary. This paper discusses the development of controllers for multi-car elevator system (MCES) using GNP The effects for MCES are examined, and we compare the advantages and performances between MCES and double-deck elevator system (DDES).

关键词： Multi-car elevator system Genetic network programming Destination floor guidance system Evolutionary computation network programming Elevators evolutionary algorithm elevator shafts control systems Cars

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Continuous Traveling Time Prediction using Genetic network programming-based Data Mining and Neural networks

Continuous Traveling Time Prediction using Genetic Network P...

引用

Society of Instrument and Control Engineers Annual Conference

作者： Qin Zhang Huiyu Zhou Shingo Mabu Kotaro Hirasawa Graduate School of Information Production and Systems Waseda University

ISBN: (纸本)9781467322591

In this paper, a method combining Genetic network programming-based class association rule mining and Neural networks is proposed for continuous traveling time prediction. Genetic network programming (GNP), as an extended algorithm of GP, shows its advantage because of its graph structures. GNP is used to generate class association rules. Then, the average matching degree of the data with the rules is calculated. Lastly, the back propagation algorithm of Neural networks is utilized in order to acquire the concrete prediction of the traveling time.

关键词： Neural network network programming data mining GP2 gene economic indicators GNPDA1 gene Travel time

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High-level abstractions for programming network policies

High-level abstractions for programming network policies

引用

作者： Yuan, Yifei University of Pennsylvania

学位级别：Ph.D.

The emergence of network programmability enabled by innovations such as active networking, SDN and NFV offers tremendous flexibility to program network policies. However, it also poses a new demand to network operators on programming network policies. The motivation of this dissertation is to study the feasibility of using high-level abstractions to simplify the programming of network policies. First, we propose scenario-based programming, a framework that allows network operators to program stateful network policies by describing example behaviors in representative scenarios. Given these scenarios, our scenario-based programming tool NetEgg automatically infers the controller state that needs to be maintained along with the rules to process network events and update state. The NetEgg interpreter can execute the generated policy implementation on top of a centralized controller, but also automatically infers flow-table rules that can be pushed to switches to improve throughput. We study a range of policies considered in the literature and report our experience regarding specifying these policies using scenarios. We evaluate NetEgg based on the computational requirements of our synthesis algorithm as well as the overhead introduced by the generated policy implementation. Our results show that our synthesis algorithm can generate policy implementations in seconds, and the automatically generated policy implementations have performance comparable to their hand-crafted implementations. Our preliminary user study results show that NetEgg was able to reduce the programming time of the policies we studied. Second, we propose NetQRE, a high-level declarative language for programming quantitative network policies that require monitoring a stream of network packets. Based on a novel theoretical foundation of parameterized quantitative regular expressions, NetQRE integrates regular-expression-like pattern matching at flow-level as well as application-level payloads with aggreg

关键词： network policy network programming

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Using TCP/IP programming to Realize the Communication among Different network Platforms

引用

Journal of Northeast Agricultural University(English Edition) 2004年第1期11卷 77-79页

作者： ZHANGChang-li SHENWei-zheng FANGJun-long EngineeringCollege.NortheastAgricuhuralUniversity HarbinHeilongjiang150030PRC

This paper mainly introduces about how to use socket to design network program which can transfer data in different network platform in terms of TCP/IP network programming Standard.

关键词： TCP/IP Socket network programming

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