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

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

引用

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 New Crossover for Solving Constraint Satisfaction Problems 1

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13th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Abbasian, Reza Mouhoub, Malek Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

ISBN: (数字)9783642371981

ISBN: (纸本)9783642371981

In this paper we investigate the applicability of genetic algorithms (GAs) for solving Constraint Satisfaction Problems (CSPs). Despite some success of GAs when tackling CSPs, they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs. Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances.

关键词： parallel genetic algorithms Constraint Satisfaction Problem (CSP) Evolutionary Techniques

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Review on parallel Evolutionary Computing and Introduce Three General Framework to parallelize All EC algorithms

Review on Parallel Evolutionary Computing and Introduce Thre...

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5th Conference on Information and Knowledge Technology (IKT)

作者： Majd, Amin Lotfi, Shahriar Sahebi, Golnaz Tabriz Univ Aras Int Campus Tabriz Iran Tabriz Univ Dept Comp Sci Tabriz Iran

ISBN: (纸本)9781467364904;9781467364898

Optimization and solving NP-hard problem are very important and Evolutionary Computing methods are useful and popular. There are different types of EC methods that most of them are serial and some other one has parallel implementation. In first step we want review some parallel implementation of EC methods and in second step we introduce three general frame work to parallelize all serial EC methods.

关键词： component parallel genetic algorithms parallel PSO parallel Ant Optimization Colony parallel Bee Colony Optimization parallel Memetic Algorithm

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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 ...

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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:...

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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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parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources

Parallel training and testing methods for complex image proc...

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Conference on parallel Processing for Imaging Applications

作者： Usamentiaga, Ruben Garcia, Daniel F. Molleda, Julio Sainz, Ignacio Bulnes, Francisco G. Univ Oviedo Dept Comp Sci Gijon 33204 Asturias Spain

ISBN: (纸本)9780819484093

Advances in the image processing field have brought new methods which are able to perform complex tasks robustly. However, in order to meet constraints on functionality and reliability, imaging application developers often design complex algorithms with many parameters which must be finely tuned for each particular environment. The best approach for tuning these algorithms is to use an automatic training method, but the computational cost of this kind of training method is prohibitive, making it inviable even in powerful machines. The same problem arises when designing testing procedures. This work presents methods to train and test complex image processing algorithms in parallel execution environments. The approach proposed in this work is to use existing resources in offices or laboratories, rather than expensive clusters. These resources are typically non-dedicated, heterogeneous and unreliable. The proposed methods have been designed to deal with all these issues. Two methods are proposed: intelligent training based on genetic algorithms and PVM, and a full factorial design based on grid computing which can be used for training or testing. These methods are capable of harnessing the available computational power resources, giving more work to more powerful machines, while taking its unreliable nature into account. Both methods have been tested using real applications.

关键词： parallelization on unreliable non-dedicated resources parallel training parallel genetic algorithms image processing based on grid computing

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

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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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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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Strong definitions of performance metrics

Strong definitions of performance metrics

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IASTED International Conference on Artificial Intelligence and Applications

作者： Konfrst, Zdenek Czech Tech Univ Fac Elect Engn Dept Cybernet CR-16635 Prague Czech Republic

ISBN: (纸本)9780889866294

As in many research works of parallel genetic algorithms (PGAs), claims of a super-linear speedup (super-linearity) have become so regular that some clarification is usually needed. This paper focuses on the estimation of computation characteristics from parallel computing. PGAs are stochastic based algorithms, so the application rules from parallel computing is not straightforward. We derive total (parallel) run times from population sizing, the estimation of selection intensity and convergence time. The flawless calculation of total run is essential for obtaining the characteristics such as speedup S(.) and others. However, although the process of derivation such characteristics is not simple, it is possible, as it is presented in the paper.

关键词： parallel genetic algorithms speedup super-linearity

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