The Experts below are selected from a list of 34884 Experts worldwide ranked by ideXlab platform

Jun Wang - One of the best experts on this subject based on the ideXlab platform.

  • A recurrent neural network for solving the shortest path Problem
    IEEE Transactions on Circuits and Systems I-regular Papers, 1996
    Co-Authors: Jun Wang
    Abstract:

    The shortest path Problem is the classical Combinatorial Optimization Problem arising in numerous planning and designing contexts. In this paper, a recurrent neural network for solving the shortest path Problem is presented. The recurrent neural network is able to generate optimal solutions to the shortest path Problem. The performance of the recurrent neural network is demonstrated by means of three illustrative examples. The recurrent neural network is shown to be capable of generating the shortest path and suitable for electronic implementation.

  • ISCAS - A recurrent neural network for solving the shortest path Problem
    Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1994
    Co-Authors: Jun Wang
    Abstract:

    The shortest path Problem is the classical Combinatorial Optimization Problem arising in numerous planning and designing contexts. In this paper, a recurrent neural network for solving the shortest path Problem is presented. The proposed recurrent neural network is able to generate optimal solutions to the shortest path Problem. The performance and operating characteristics of the recurrent neural network are demonstrated by use of illustrative examples. >

N. Yamamoto - One of the best experts on this subject based on the ideXlab platform.

  • selecting fuzzy if then rules for classification Problems using genetic algorithms
    IEEE Transactions on Fuzzy Systems, 1995
    Co-Authors: H. Ishibuchi, K. Nozaki, N. Yamamoto, Hideo Tanaka
    Abstract:

    This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection Problem is formulated as a Combinatorial Optimization Problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this Problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the Combinatorial Optimization Problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher. >

  • Selecting fuzzy rules by genetic algorithm for classification Problems
    [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 1993
    Co-Authors: H. Ishibuchi, K. Nozaki, N. Yamamoto
    Abstract:

    The authors propose a genetic algorithm method for choosing an appropriate set of fuzzy if-then rules for classification Problems. The aim of the proposed method is to find a minimum set of fuzzy if-then rules that can correctly classify all training patterns. This is achieved by formulating and solving a Combinatorial Optimization Problem that has two objectives, which are to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. A genetic algorithm was applied to this Problem and simulation results are shown. An individual (i.e., a solution) in the genetic algorithm is the set of fuzzy if-then rules, and its fitness is determined by the two objectives in the Combinatorial Optimization Problem.

Ichiro Iimura - One of the best experts on this subject based on the ideXlab platform.

  • Study on Quantum Parallel Processing by Adiabatic Quantum Computation in Simon Problem
    2013 International Conference on Parallel and Distributed Computing Applications and Technologies, 2013
    Co-Authors: Shigeru Nakayama, Peng Gang, Ichiro Iimura
    Abstract:

    Adiabatic quantum computation has been proposed as quantum parallel processing with adiabatic evolution by using a superposition state to solve Combinatorial Optimization Problem, then it has been applied to many Problems like satisfiability Problem. Among them, Deutsch and Deutsch-Jozsa Problems have been tried to be solved by using adiabatic quantum computation. In this paper, we modify the adiabatic quantum computation and propose to solve Simon Problem more efficiently by a method with higher observation probability.

  • Study on Quantum Parallel Processing by Adiabatic Quantum Computation in Bernstein-Vazirani Problem
    2012 13th International Conference on Parallel and Distributed Computing Applications and Technologies, 2012
    Co-Authors: Shigeru Nakayama, Peng Gang, Ichiro Iimura
    Abstract:

    Adiabatic quantum computation has been proposed as quantum parallel processing with adiabatic evolution by using a superposition state to solve Combinatorial Optimization Problem, then it has been applied to many Problems like satisfiability Problem. Among them, Deutsch and Deutsch-Jozsa Problems have been tried to be solved by using adiabatic quantum computation. In this paper, we modify the adiabatic quantum computation and propose to solve Bernstein-Vazirani Problem more efficiently by a method with higher observation probability.

H. Ishibuchi - One of the best experts on this subject based on the ideXlab platform.

  • selecting fuzzy if then rules for classification Problems using genetic algorithms
    IEEE Transactions on Fuzzy Systems, 1995
    Co-Authors: H. Ishibuchi, K. Nozaki, N. Yamamoto, Hideo Tanaka
    Abstract:

    This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection Problem is formulated as a Combinatorial Optimization Problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this Problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the Combinatorial Optimization Problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher. >

  • Selecting fuzzy rules by genetic algorithm for classification Problems
    [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 1993
    Co-Authors: H. Ishibuchi, K. Nozaki, N. Yamamoto
    Abstract:

    The authors propose a genetic algorithm method for choosing an appropriate set of fuzzy if-then rules for classification Problems. The aim of the proposed method is to find a minimum set of fuzzy if-then rules that can correctly classify all training patterns. This is achieved by formulating and solving a Combinatorial Optimization Problem that has two objectives, which are to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. A genetic algorithm was applied to this Problem and simulation results are shown. An individual (i.e., a solution) in the genetic algorithm is the set of fuzzy if-then rules, and its fitness is determined by the two objectives in the Combinatorial Optimization Problem.

K. Li - One of the best experts on this subject based on the ideXlab platform.

  • Job scheduling for grid computing on metacomputers
    19th IEEE International Parallel and Distributed Processing Symposium, 2005
    Co-Authors: K. Li
    Abstract:

    Scheduling is a fundamental issue in achieving high performance on metacomputers and computational grids. For the first time, the job scheduling Problem for grid computing on metacomputers is studied as a Combinatorial Optimization Problem. It is proven that the list scheduling algorithm can achieve reasonable worst-case performance bound in grid environments supporting distributed super computing with large applications. It is also observed that communication heterogeneity does have significant impact on schedule lengths.