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检索条件"主题词=Parallel Genetic Algorithm"

共 262 条记录，以下是91-100 订阅

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The Design and Implementation of MPI Master-Slave parallel genetic algorithm

The Design and Implementation of MPI Master-Slave Parallel G...

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4th International Conference on Graphic and Image Processing (ICGIP)

作者： Liu, Shuping Cheng, Yanliu North China Elect Power Univ Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9780819495662

In this paper, the MPI master-slave parallel genetic algorithm is implemented by analyzing the basic genetic algorithm and parallel MPI program, and building a Linux cluster. This algorithm is used for the test of maximum value problems (Rosen brocks function). And we acquire the factors influencing the master-slave parallel genetic algorithm by deriving from the analysis of test data. The experimental data shows that the balanced hardware configuration and software design optimization can improve the performance of system in the complexity of the computing environment using the master-slave parallel genetic algorithms.

关键词： MPI Cluster parallel genetic algorithm

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A Multi-Population Based parallel genetic algorithm for Multiprocessor Task Scheduling with Communication Costs

A Multi-Population Based Parallel Genetic Algorithm for Mult...

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IEEE Symposium on Computers and Communication (ISCC)

作者： Morady, Rashid Dal, Deniz Ataturk Univ Dept Comp Engn Fac Engn TR-25240 Erzurum Turkey

ISBN: (纸本)9781509006793

Multiprocessor task scheduling is one of the hardest combinatorial optimization problems in parallel and distributed systems. It is known as NP-hard and therefore, scanning the whole search space using an exact algorithm to find the optimal solution is not practical. Instead, metaheuristics are mostly employed to find a near-optimal solution in a reasonable amount of time. In this paper, a multi-population based parallel genetic algorithm is presented for the optimization of multiprocessor task scheduling in the presence of communication costs. To the best of our knowledge, this parallel genetic algorithm approach is applied to the problem at hand for the first time using a benchmark set that includes well-known task graphs from different sources. Our experiments conducted on several task graphs with different sizes from the benchmark set showed the superiority of the approach over a conventional genetic algorithm and the related works in the literature in terms of two different performance metrics. Our parallel implementation not only decreased the execution time but also increased the quality of the scheduling solutions considerably.

关键词： Directed Acyclic Graph DAG Task Graph Multiprocessor Scheduling Task Scheduling Communication Cost Multi-population parallel genetic algorithm Island Model MPI

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Generic parallel genetic algorithm Framework for Protein Optimisation

Generic Parallel Genetic Algorithm Framework for Protein Opt...

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11th International Conference on algorithms and Architectures for parallel Processing (ICA3PP)

作者： Folkman, Lukas Pullan, Wayne Stantic, Bela Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld 4111 Australia

ISBN: (纸本)9783642246685

Proteins are one of the most vital macromolecules on the cellular level. In order to understand the function of a protein, its structure needs to be determined. For this purpose, different computational approaches have been introduced. genetic algorithms can be used to search the vast space of all possible conformations of a protein in order to find its native structure. A framework for design of such algorithms that is both generic, easy to use and performs fast on distributed systems may help further development of genetic algorithm based approaches. We propose such a framework based on a parallel master-slave model which is implemented in C++ and Message Passing interface. We evaluated its performance on distributed systems with a different number of processors and achieved a linear acceleration in proportion to the number of processing units.

关键词： parallel genetic algorithm Protein Optimisation Protein Structure Prediction

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An Asynchronous parallel genetic algorithm for the Maximum Likelihood Phylogenetic Tree Search

An Asynchronous Parallel Genetic Algorithm for the Maximum L...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Tsuji, Miwako Sato, Mitsuhisa Tanabe, Akifumi S. Inagaki, Yuji Hashimoto, Tetsuo Univ Tsukuba Ctr Computat Sci Ibaraki Japan Kyoto Univ Grad Sch Sci Kyoto 6068501 Japan Univ Tsukuba Inst Biol Sci Tsukuba Ibaraki 305 Japan

ISBN: (纸本)9781467315098

A phylogenetic tree represents the evolutionary relationships among biological species. Although parallel computation is essential for the phylogenetic tree searches, it is not easy to maintain the diversity of population in a parallel genetic algorithm. In this paper, we design a new asynchronous parallel genetic algorithm for tree optimization which maintain the diversity of population without any communication or synchronization.

关键词： A phylogenetic tree parallel genetic algorithm asynchronous

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DGA Fault Diagnosis Based on the Counter Propagation Neural Network Optimized by parallel genetic algorithm

DGA Fault Diagnosis Based on the Counter Propagation Neural ...

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IEEE International Conference of Region 10 (TENCON)

作者： Zhao, An-Xin Zhang, Cai-Tian Xian Univ Sci & Technol Network Ctr Xian Peoples R China HeNan Econ & Trade Vocat Coll Dept Elect Engn Zhengzhou Peoples R China

ISBN: (纸本)9781479928255;9781479928279

Using the counter propagation artificial neural network (CPANN) to diagnose the DGA fault, network structural parameters should be set, such as the training epochs, network size etc. When user to set, it would be affect by the artificial subjective factors. If we use the traversal search way, it would be the consumption of computing and time. So this article employed parallel genetic algorithm to optimize network structure optimization parameters of counter propagation neural network. genetic algorithm is a simulation Darwin the evolution natural selection and genetic mechanism of biological evolution process calculation model, and a by simulating natural evolution to search the optimal solution. In the GA procedure, the fitness function was defined by the correct ratio combination of the calibration data set and validation data set, as the rules for selecting the optimal network parameters. When selecting the optimal network parameters, the relatively high repeated frequency of chromosome and the optimal fitness function values simultaneously were considered.

关键词： dissolved gas-in-oil analysis (DGA) fault diagnosis Counter-Propagation Artificial Neural Networks (CPANN) parallel genetic algorithm

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A Coarse-grained parallel genetic algorithm with Migration for Shortest Path Routing Problem

A Coarse-grained Parallel Genetic Algorithm with Migration f...

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11th IEEE International Conference on High Performance Computing and Communications

作者： Yussof, Salman Razali, Rina Azlin See, Ong Hang Ghapar, Azimah Abdul Din, Marina Md

ISBN: (纸本)9781424446001

Shortest path routing is the ope of routing widely used in computer networks nowadays. Even though shortest path routing algorithms are well established, other alternative methods may have their own advantages. One such alternative is to use a GA-based routing algorithm. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies, However, it is also known that GA-based routing algorithm is not fast enough for real-time computation. In this paper, we proposed a coarse-grained parallel genetic algorithm for solving the shortest path routing problem with the aim to reduce its computation time. The migration scheme, which is commonly used in coarse-grained parallel genetic algorithm, is also employed in the proposed algorithm. This algorithm is developed and run an an MPI cluster. This paper studies the effect of migration on the proposed algorithm and the performance of the algorithm as compared to its serial counterpart.

关键词： Coarse grained message passing interface parallel genetic algorithm shortest path routing

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Solving Cryptarithmetic Problems Using parallel genetic algorithm

Solving Cryptarithmetic Problems Using Parallel Genetic Algo...

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2nd International Conference on Computer and Electrical Engineering

作者： Abbasian, Reza Mazloom, Masoud Shahid Chamran Univ Dept Comp Engn Ahvaz Iran

ISBN: (纸本)9780769539256

Cryptarithmetic is a class of constraint satisfaction problems which includes making mathematical relations between meaningful words using simple arithmetic operators like 'plus' in a way that the result is conceptually true, and assigning digits to the letters of these words and generating numbers in order to make correct arithmetic operations as well. A simple way to solve such problems is by depth first search (DFS) algorithm which has a big search space even for quite small problems. In this paper we proposed a solution to this problem with genetic algorithm and then optimized it by using parallelism. We also showed that the algorithm reaches a solution faster and in a smaller number of iterations than similar algorithms.

关键词： constraint satisfaction cryptarithmetic parallel genetic algorithm

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A parallel genetic algorithm approach for automated discovery of censored production rules

A parallel genetic algorithm approach for automated discover...

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IASTED International Conference on Artificial Intelligence and Applications

作者： Saroj Bharadwaj, K. K. Guru Jambheshwar Univ Sci & Technol Dept Comp Sci & Engn Hisar 125001 Haryana India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (纸本)9780889866294

Exceptions, which focus on a very small portion of dataset, have been discarded as noise in machine learning. It is interesting to discover exceptions, as they challenge the existing knowledge and often lead to the growth of knowledge in new directions. Discovering exceptions from voluminous data sets still remains a great challenge. In Knowledge Discovery in Databases (KDD), it is also significant to extract the knowledge in a form that is flexible and efficient enough for approximate reasoning. A Censored Production Rule (CPR) is a special kind of knowledge structure that is represented as an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to rule. If P Then D part of CPR holds frequently while Unless C part holds rarely. Discovery of CPRs not only provides a computational mechanism to exhibit variable precision logic but also results in the discovery of a set of rules of considerably reduced size. In this paper, a parallel genetic algorithm approach is suggested for automated discovery of Censored Production Rules. The parallel genetic algorithm involves both data and control parallelism. A fitness function that incorporates the constraints of CPRs, is proposed. The experimental results establish the effectiveness of the proposed algorithm.

关键词： data mining parallel genetic algorithm censored production rules variable precision logic

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Predictive Job Scheduling in a Connection Limited System using parallel genetic algorithm

Predictive Job Scheduling in a Connection Limited System usi...

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International Conference on Intelligent and Advanced Systems

作者： Neduncheliyan, S. Pramod, S. Anna Univ JJ Coll Engn & Technol Dept Comp Sci & Engn Tiruchirappalli 620009 Tamil Nadu India

ISBN: (纸本)9781424413553

Job scheduling is the key feature of any computing environment and the efficiency of computing depends largely on the scheduling technique used. Intelligence is the key factor which is lacking in the job scheduling techniques of today. genetic algorithms are powerful search techniques based on the mechanisms of natural selection and natural genetics. Multiple jobs are handled by the scheduler and the resource the job needs are in remote locations. Here we assume that the resource a job needs are in a location and not split over nodes and each node that has a resource runs a fixed number of jobs. The existing algorithms used are non predictive and employs greedy based algorithms or a variant of it. The efficiency of the job scheduling process would increase if previous experience and the genetic algorithms are used. In this paper, A new technique is proposed as a model of the scheduling algorithm where the scheduler can learn from previous experiences and an effective job scheduling is achieved as time progresses.

关键词： Job scheduling remote resource parallel genetic algorithm

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Sparsely Synchronized parallel genetic algorithm for Road Traffic Network Division 8

Sparsely Synchronized Parallel Genetic Algorithm for Road Tr...

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8th International Conference on Human System Interactions (HSI)

作者： Potuzak, Tomas Univ W Bohemia Fac Sci Appl Dept Comp Sci & Engn NTIS European Ctr Excellence Plzen 30614 Czech Republic

ISBN: (纸本)9781467369367

In this paper, we explore the features of the sparsely synchronized parallel genetic algorithm for the road traffic network division. The algorithm is an alternative to a commonly used island model for the parallelization of the genetic algorithms. The algorithm employs the parallelization of particular phases of the genetic algorithm (fitness values calculation, crossover, etc.). However, the threads of the genetic algorithm are not synchronized in every generation, but rather only once per several generations or even not at all. The lack of the synchronization leads to the inconsistencies in the shared memory, which does not have to be a problem considering the stochastic nature of the genetic algorithms. The investigation of the features and usability of the sparse synchronization of the parallel genetic algorithm (with application for the road traffic network division) is the main theme of this paper.

关键词： parallel genetic algorithm traffic network division sparse synchronization inconsistency

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