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 Programmin...
详细信息
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.
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 qua...
详细信息
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.
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 st...
详细信息
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.
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 ...
详细信息
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.
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, t...
详细信息
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.
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 doub...
详细信息
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.
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 buildin...
详细信息
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).
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 exte...
详细信息
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.
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 operator...
详细信息
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 programmingnetwork 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
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.
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.
暂无评论