The Experts below are selected from a list of 72624 Experts worldwide ranked by ideXlab platform
Michal Przewozniczek - One of the best experts on this subject based on the ideXlab platform.
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active multi population pattern Searching Algorithm for flow optimization in computer networks the novel coevolution schema combined with linkage learning
Information Sciences, 2016Co-Authors: Michal PrzewozniczekAbstract:The proposition of new effective evolutionary method dedicated to solve flow optimization problems in computer networks.The proposition of new flexible virus population handling mechanisms in novel MuPPetS method.Tests performed for practical problem with very large search space. The main objective of this paper is to propose an effective evolutionary method for solving the problem of working paths optimization in survivable MPLS network. The paper focuses on existing network, in which only network flow can be optimized to provide network survivability using the local repair strategy. The problem is NP-complete, the solution space of the test cases is large and many genes are required to code the potential solution. Recently, the MuPPetS method (Multi-Population Pattern Searching Algorithm for Flow Assignment) was proposed and seems to be a promising tool for tackling high-dimensional, hard optimization problems. The MuPPetS is a linkage learning method that minimizes the negative effects of typical EA bottlenecks, e.g., preconvergence and significant effectiveness dropdown caused by an increasing number of genes in the chromosome. In comparison to other evolutionary methods, the MuPPetS was shown to be effective and capable of solving GA-hard problems. Therefore, the proposed MuPPetS-FuN method (Multi-Population Pattern Searching Algorithm for Flow Assignment in Non-bifurcated Commodity Flow) is based on MuPPetS. The additional objective of this paper is to propose changes to general MuPPetS framework to increase its effectiveness via better subpopulation number control strategy.
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multi population pattern Searching Algorithm a new evolutionary method based on the idea of messy genetic Algorithm
IEEE Transactions on Evolutionary Computation, 2011Co-Authors: Halina Kwasnicka, Michal PrzewozniczekAbstract:One of the main evolutionary Algorithms bottlenecks is the significant effectiveness dropdown caused by increasing number of genes necessary for coding the problem solution. In this paper, we present a multi population pattern Searching Algorithm (MuPPetS), which is supposed to be an answer to situations where long coded individuals are a must. MuPPetS uses some of the messy GA ideas like coding and operators. The presented Algorithm uses the binary coding, however the objective is to use MuPPetS against real-life problems, whatever coding schema. The main novelty in the proposed Algorithm is a gene pattern idea based on retrieving, and using knowledge of gene groups which contains genes highly dependent on each other. Thanks to gene patterns the effectiveness of data exchange between population individuals improves, and the Algorithm gains new, interesting, and beneficial features like a kind of “selective attention” effect.
Pla Information - One of the best experts on this subject based on the ideXlab platform.
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implementation of Searching Algorithm of the ready task and priority inversion in embedded rtos
Computer Engineering, 2003Co-Authors: Guo Yu, Pla InformationAbstract:In this paper,the authors aim at Searching of the most priority ready task in embedded RTOS, and put forward a efficient Searching Algorithm, which improves Searching speed of the ready task. At the same time, the paper gives a useful implementation about inversion of different priority tasks.
Christof Zalka - One of the best experts on this subject based on the ideXlab platform.
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GROVER'S QUANTUM Searching Algorithm IS OPTIMAL
Physical Review A, 1999Co-Authors: Christof ZalkaAbstract:I show that for any number of oracle lookups up to about $\ensuremath{\pi}/4\sqrt{N},$ Grover's quantum Searching Algorithm gives the maximal possible probability of finding the desired element. I explain why this is also true for quantum Algorithms which use measurements during the computation. I also show that unfortunately quantum Searching cannot be parallelized better than by assigning different parts of the search space to independent quantum computers.
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grover s quantum Searching Algorithm is optimal
Physical Review A, 1999Co-Authors: Christof ZalkaAbstract:I show that for any number of oracle lookups up to about {pi}/4thinsp{radical} (N) , Grover{close_quote}s quantum Searching Algorithm gives the maximal possible probability of finding the desired element. I explain why this is also true for quantum Algorithms which use measurements during the computation. I also show that unfortunately quantum Searching cannot be parallelized better than by assigning different parts of the search space to independent quantum computers. {copyright} {ital 1999} {ital The American Physical Society}
Guo Yu - One of the best experts on this subject based on the ideXlab platform.
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implementation of Searching Algorithm of the ready task and priority inversion in embedded rtos
Computer Engineering, 2003Co-Authors: Guo Yu, Pla InformationAbstract:In this paper,the authors aim at Searching of the most priority ready task in embedded RTOS, and put forward a efficient Searching Algorithm, which improves Searching speed of the ready task. At the same time, the paper gives a useful implementation about inversion of different priority tasks.
H.q. Cao - One of the best experts on this subject based on the ideXlab platform.
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Optimized multilevel codebook Searching Algorithm for vector quantization in image coding
Visual Communications and Image Processing '96, 1996Co-Authors: H.q. CaoAbstract:An optimized multi-level codebook Searching Algorithm (MCS) for vector quantization is presented in this paper. Although it belongs to the category of the fast nearest neighbor Searching (FNNS) Algorithms for vector quantization, the MCS Algorithm is not a variation of any existing FNNS Algorithms (such as k-d tree Searching Algorithm, partial-distance Searching Algorithm, triangle inequality Searching Algorithm...). A multi-level search theory has been introduced. The problem for the implementation of this theory has been solved by a specially defined irregular tree structure which can be built from a training set. This irregular tree structure is different from any tree structures used in TSVQ, prune tree VQ, quad tree VQ... Strictly speaking, it cannot be called tree structure since it allows one node has more than one set of parents, it is only a directed graph. This is the essential difference between MCS Algorithm and other TSVQ Algorithms which ensures its better performance. An efficient design procedure has been given to find the optimized irregular tree for practical source. The simulation results of applying MCS Algorithm to image VQ show that this Algorithm can reduce Searching complexity to less than 3% of the exhaustive search vector quantization (ESVQ) (4096 codevectors and 16 dimension) while introducing negligible error (0.064 dB degradation from ESVQ). Simulation results also show that the Searching complexity is close linearly increase with bitrate.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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ISCAS - A new multilevel codebook Searching Algorithm for vector quantization
Proceedings of ISCAS'95 - International Symposium on Circuits and Systems, 1Co-Authors: H.q. CaoAbstract:A new multilevel codebook Searching (MCS) Algorithm for vector quantization is presented. Although it belongs to the category of the fast nearest neighbor Searching (FNNS) Algorithms for vector quantization, the new MCS Algorithm is not a variation of any existing FNNS Algorithms (such as k-d tree Searching Algorithm, partial distance Searching Algorithm, triangle inequality Searching Algorithm...). The Searching strategy involves several search levels. Each level stores a certain size codebook. Searching starts from the stage containing the smallest size (lowest bitrate) codebook to the level containing largest size (highest bitrate) codebook. The Searching paths between any two adjacent levels are built by using training sets. The simulation result of applying MCS Algorithm to image VQ shows that the MCS Algorithm can reduce Searching complexity to less than 3% of an exhaustive Searching VQ (ESVQ) (codebook size of 4096) while introducing negligible error (0.064 db degradation from ESVQ). A comparison between the MCS Algorithm and several k-d binary tree Searching Algorithms is presented too. The MCS Algorithm fits very well into multilevel codebook VQ in the vector transform and vector sub-band domains.
