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检索条件"主题词=Parallel genetic algorithms"

共 111 条记录，以下是91-100 订阅

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Towards Cost-Effective Bio-inspired Optimization: A Prospective Study on the GPU Architecture

引用

2nd Swarm, Evolutionary and Memetic Computing Conference (SEMCCO 2011)

作者： Prata, Paula Fazendeiro, Paulo Sequeira, Pedro Univ Beira Interior Dept Informat Covilha Portugal

ISBN: (纸本)9783642272417

This paper studies the impact of varying the population's size and the problem's dimensionality in a parallel implementation, for an NVIDIA GPU, of a canonical GA. The results show that there is an effective gain in the data parallel model provided by modern GPU's and enhanced by high level languages such as OpenCL. In the reported experiments it was possible to obtain a speedup higher than 140 thousand times for a population's size of 262 144 individuals.

关键词： GPGPU parallel genetic algorithms OpenCL data parallelism

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Partial Decomposition and parallel GA (PD-PGA) for Constrained Optimization

Partial Decomposition and Parallel GA (PD-PGA) for Constrain...

引用

IEEE International Conference on System, Man, and Cybernetic

作者： Elfeky, Ehab Z. Sarker, Ruhul A. Essam, Daryl L. UNSW ADFA Sch IT&EE Canberra ACT Australia

ISBN: (纸本)9781424423835

Large scale constrained optimization problem solving is a challenging research topic in the optimization and computational intelligence domain. This paper examines the possible division of computational tasks, into smaller interacting components, in order to effectively solve constrained optimization problems in the continuous domain. In dividing the tasks, we propose problem decomposition, and the use of GAs as the solution approach. In this paper, we consider problems wit h block angular structure with or without overlapping variables. We decompose not only the problem but also the chromosome as suitable for different components of the problem. We also design a communication process for exchanging information between the components. The research shows an approach of dividing computation tasks, required in solving large scale optimization problems, which can be processed in parallel machines. A number of test problems have been solved to demonstrate the use of the proposed approach. The results are very encouraging.

关键词： Large-scale constrained continuous optimization parallel genetic algorithms

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On the use of rule-sharing in learning classifier system ensembles

On the use of rule-sharing in learning classifier system ens...

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IEEE Congress on Evolutionary Computation

作者： Bull, L Studley, M Bagnall, T Whittley, I Univ W England Sch Comp Sci Bristol BS16 1QY Avon England

ISBN: (纸本)0780393635

This paper presents an investigation into exploiting the population-based nature of Learning Classifier Systems for their use within highly-parallel systems. In particular, the use of simple accuracy-based Learning Classifier Systems within the ensemble machine approach is examined. Results indicate that inclusion of a rule migration mechanism inspired by parallel genetic algorithms is an effective way to improve learning speed.

关键词： genetic algorithms learning (artificial intelligence) parallel algorithms pattern classification ensemble machine learning classifier system parallel genetic algorithms population-based nature rule migration mechanism rule-sharing genetic Algorithm (GA) parallel algorithms Pattern recognition Classifiers Learning learning systems Ensembles

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A State-of-the-art Review of Population-based parallel Meta-heuristics

A State-of-the-art Review of Population-based Parallel Meta-...

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World Congress on Nature and Biologically Inspired Computing

作者： Madhuri Deep, Kusum Indian Inst Technol Dept Math Roorkee 247667 Uttarakhand India

ISBN: (纸本)9781424450534

Mathematical models of many real life optimization problems turn out to be so complex that traditional optimization techniques such as gradient based methods and other deterministic techniques etc are not applicable for them. A new class of optimization techniques called population-based meta-heuristics (PBM) is applied for the solution of these problems. Particle Swarm Optimization algorithm (PSO) and genetic Algorithm (GA) are two of the most popular algorithms in this category and have been extensively used in recent years for the solution of this kind of optimization problems. But for these techniques, computational cost (measured by elapsed time) is too high. Fortunately, these techniques have inherent parallelism in them. This inspires many researchers to implement them on the latest parallel computers. This review paper tries to present a state-of-the-art in parallel genetic algorithms and parallel particle swarm optimization.

关键词： parallel genetic algorithms parallel particle swarm optimization parallel meta-heuristics

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Implementation of parallel Evolutionary Convolutional Neural Network for Classification in Human Activity and Image Recognition 22nd

Implementation of Parallel Evolutionary Convolutional Neural...

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22nd Mexican International Conference on Artificial Intelligence (MICAI)

作者： Villegas-Cortez, Juan Roman-Alonso, Graciela Fernandez De Vega, Francisco Flores-Morales, Yafte Aaron Cordero-Sanchez, Salomon Univ Autonoma Metropolitana Dept Sistemas Azapotzalco Av Sn Pablo 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Ingn Elect Unidad Iztapalapa Mexico City DF Mexico Univ Extremadura Dept Comp Sci C Santa Teresa Jornet38 Merida 06800 Spain Univ Autonoma Metropolitana Unidad Iztapalapa Dept Chem Mexico City DF Mexico

ISBN: (纸本)9783031477645;9783031477652

Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. Processing this volume of information allows a more exact classification in wider types of applications;however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several Machine Learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep Learning.

关键词： Deep Learning Pattern recognition HAR Image Recognition parallel genetic algorithms

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Framework for genetic algorithms Using Pilot Jobs in Adaptive Grid Workflows

Framework for Genetic Algorithms Using Pilot Jobs in Adaptiv...

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9th International Conference on Large-Scale Scientific Computations (LSSC)

作者： Jakimovski, Boro Ilijoski, Bojan Velinov, Goran Sahpaski, Dragan Ss Cyril & Methodius Univ Skopje Fac Comp Sci & Engn Skopje Macedonia

ISBN: (纸本)9783662438800;9783662438794

The performance of Grid applications may be very unstable, especially when using workflows for job distribution. This is mainly due to the Grid overheads, like scheduling and queuing, introduced before the job is executed on a worker node. Optimization problems using genetic algorithms (GAs) can be easily and efficiently implemented on Grids using Grid workflows. Due to the file dependencies introduced in the Grid workflows for GAs, mainly for genetic material interchange, these overheads are cumulative and thus very noticeable. This problem is also very evident when the jobs are short compared to the Grid overheads, i.e. the job spends more time waiting in a queue to be executed than the execution itself. In this paper we introduce a framework that enables users to easily utilize the Grid infrastructure for their optimization using GAs. It allows a user to preallocate certain number of pilot jobs, and also to dynamically manage their number for optimal availability of resources during the optimization process. In this way, once an application starts to execute the workloads, it will have at least one available pilot for execution of pooled tasks. This introduces better utilization of the Grid resources, as well boost the confidence in the infrastructure from users point of view.

关键词： parallel genetic algorithms Grid infrastructure Pilot jobs

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Empire Establishment Algorithm

Empire Establishment Algorithm

引用

International Conference on Computer Technology and Development

作者： Saif, Seyed Mojtaba Shah-Hosseini, Hamed Feizi, Mohammad Reza Islamic Azad Univ Safa Shahr Branch Open Univ Safa Shahr Safa Shahr Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Open Univ Sci & Res Comp Engn Tehran Iran Azad Univ Sci & Res Branch Tehran Iran

ISBN: (纸本)9780769538921

The main goal of the research has described in this paper is to change the simple GA (genetic Algorithm) for better accuracy. The proposed Empire Establishment Algorithm is similar to the parallel genetic Algorithm. genetic algorithms are a subclass of Evolutionary algorithms. genetic algorithms mimic some processes of nature. Here, with the distribution fashion of the tribes in several regions and empire establishment, some changes in the simple genetic Algorithm are proposed. These changes will add to the accuracy and the success of the GA.

关键词： Meta-Heuristics genetic algorithms parallel genetic algorithms N-Queen Problem

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Search space extension and PGAs: A comparative study of parallelization schemes to genetic algorithms using the TSP

Search space extension and PGAs: A comparative study of para...

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IASTED International Conference on Artificial Intelligence and Applications

作者： Davoian, K Univ Munster Dept Math & Comp Sci D-48149 Munster Germany

ISBN: (纸本)0889865566

parallel genetic algorithms (PGAs) have been developed to reduce the execution time of serial genetic algorithms (SGA) and to solve larger problems. They typically find better solutions, as they are likely to be more resistant to premature convergence to local minima compared to serial genetic algorithms. This paper presents a comparative study of four different PGAs using the traveling salesman problem (TSP) as the case application in order to quantify their performance on the basis of small initial populations. Besides the well-known parallelization approaches, a new parallelization scheme is considered which combines iterative data exchanges and new solutions generation, thus extending a search space during the evolution. To make the comparison fair, all PGAs are using the same baseline serial genetic algorithm, started from the same set of initial populations and tested on the same known instances from the TSPLIB-archive.

关键词： parallel genetic algorithms traveling salesman problem genetic algorithms

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Migration Effects on Tree Topology of parallel Evolutionary Computation

Migration Effects on Tree Topology of Parallel Evolutionary ...

引用

IEEE Region 10 Conference on TENCON 2010

作者： Miyagi, Hayato Tengan, Takeshi Mohamed, Said Nakamura, Morikazu Univ Ryukyus Dept Informat Eng Okinawa 9030213 Japan Meio Univ Fac Int Studies Okinawa 9058585 Japan

ISBN: (纸本)9781424468904

Migration of individuals among islands is effective in many cases in distributed parallel processing of the evolutionary computation. However, the effectiveness of migration depends on many factors such as migration frequency, migration topologies, migration scale, timing, and so on. The objective of our research is to investigate such factors for designing efficient parallel evolutionary computation. In this paper we analyzes the migration effects of the tree based island model in parallel genetic algorithms and parallel PBIL (Population Based Incremental Learning). Computational experiment shows us migration effects of the parallel evolutionary computation and influence of the migration topology and frequency.

关键词： distributed parallel processing genetic algorithms learning (artificial intelligence) migration effects parallel PBIL parallel evolutionary computation parallel genetic algorithms parallel processing population based incremental learning topology tree bas

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A Comment on Bio-inspired Optimisation via GPU Architecture: The genetic Algorithm Workload

A Comment on Bio-inspired Optimisation via GPU Architecture:...

引用

3rd International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO)

作者： Prata, Paula Fazendeiro, Paulo Sequeira, Pedro Padole, Chandrashekhar Univ Beira Interior Dept Informat Inst Telecomunicacoes IT Portugal Covilha Portugal

ISBN: (纸本)9783642353796;9783642353802

This paper characterizes a genetic algorithm based on the analysis of the workload of its operators. Different granular parallel implementations of a genetic algorithm in the GPU architecture are compared against the correspondent sequential version. With the help of three benchmark problems, a complete characterization of the relative execution times of the genetic operators, varying the population cardinality and the genotype size, is offered. The best speedups, obtained with large populations, are higher than one thousand times faster than the corresponding sequential version. The assessment of different granularity levels shows that the two-dimensional parallelism supported by the GPU architecture is valuable for the crossover operator.

关键词： GPGPU parallel genetic algorithms OpenCL data parallelism

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