检索结果-内蒙古大学图书馆

IEEE Congress on Evolutionary Computation (CEC)

作者： Munawar, Asim Wahib, Mohamed Munetomo, Masaharu Akama, Kiyoshi Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Informat Initiat Ctr Informat Syst Design Lab Sapporo Hokkaido Japan

ISBN: (纸本)9781424478354

In this paper we propose a many-core implementation of evolutionary computation for GPGPU (General-Purpose Graphic Processing Unit) to solve non-convex Mixed Integer Non-Linear Programming (MINLP) and non-convex Non Linear Programming (NLP) problems using a stochastic algorithm. Stochastic algorithms being random in their behavior are difficult to implement over GPU like architectures. In this paper we not only succeed in implementation of a stochastic algorithm over GPU but show considerable speedups over CPU implementations. The stochastic algorithm considered for this paper is an adaptive resolution approach to genetic algorithm (arGA), developed by the authors of this paper. The technique uses the entropy measure of each variable to adjust the intensity of the genetic search around promising individuals. Performance is further improved by hybridization with adaptive resolution local search (arLS) operator. In this paper, we describe the challenges and design choices involved in parallelization of this algorithm to solve complex MINLPs over a commodity GPU using Compute Unified Device Architecture (CUDA) programming model. Results section shows several numerical tests and performance measurements obtained by running the algorithm over an nVidia Fermi GPU. We show that for difficult problems we can obtain a speedup of up to 20x with double precision and up to 42x with single precision.

关键词： Adaptive Resolution genetic Algorithm parallel genetic algorithms General-Purpose computation on Graphics Processing Units (GPGPU) Compute Unified Device Architecture (CUDA)

来源：评论

学校读者我要写书评

暂无评论

OpenCL Implementations of a genetic Algorithm for Feature Selection in Periocular Biometric Recognition

OpenCL Implementations of a Genetic Algorithm for Feature Se...

引用

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

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

ISBN: (纸本)9783642353796;9783642353802

This paper explores OpenCL implementations of a genetic algorithm used to optimize the features vector in periocular biometric recognition. Using a multi core platform the algorithm is tested for CPU and GPU, exploring different parallelization levels for each operator of the genetic algorithm. The results show that using the GPU platform it is possible to accelerate the algorithm by several orders of magnitude, with a recognition rate similar to the one obtained in the sequential version. The results also show that it is possible to use only a small portion of the features without any degradation of the classifier's recognition rate.

关键词： GPGPU parallel genetic algorithms Biometric Recognition Periocular Recognition OpenCL Data parallelism

来源：评论

学校读者我要写书评

暂无评论

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

Implementation of Parallel Evolutionary Convolutional Neural...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Multiobjective VLSI cell placement using distributed genetic algorithm 05

Multiobjective VLSI cell placement using distributed genetic...

引用

genetic and Evolutionary Computation Conference

作者： Sait, Sadiq M. Faheemuddin, Mohammed Minhas, Mahmood R. Sanaullah, Syed King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia

ISBN: (纸本)1595930108

genetic algorithms have worked fairly well for the VLSI cell placement problem, albeit with significant run times. Two parallel models for GA are presented for VLSI cell placement where the objectives axe optimizing power dissipation, timing performance and interconnect wirelength, while layout width is a constraint. A Master-Slave approach is mentioned wherein both fitness calculation and crossover mechanism are distributed among slaves. A Multi-Deme parallel GA is also presented in which each processor works independently on an allocated subpopulation followed by information exchange through migration of chromosomes. A pseudo-diversity approach is taken, wherein similar solutions with the same overall cost values are not permitted in the population at any given time. A series of experiments are performed on ISCAS-85/89 benchmarks to show the performance of the Multi-Deme approach.

关键词： algorithm performance parallel genetic algorithms cluster computing fuzzy logic genetic crossover

来源：评论

学校读者我要写书评

暂无评论

A robust technique for recovery of epipolar geometry by fine-grain parallel GA

A robust technique for recovery of epipolar geometry by fine...

引用

5th Joint Conference on Information Sciences (JCIS 2000)

作者： Taghiyareh, F Nagahashi, H Tokyo Inst Technol Imaging Sci & Eng Lab Midori Ku Yokohama Kanagawa 2268503 Japan

ISBN: (纸本)0964345692

This paper addresses the problem of computing the three-dimensional motions of objects and proposes a robust approach to position estimation of moving objects by exploiting the only available geometric constraint, namely, the epipolar constraint. The extrinsic parameters of the camera and the motion of the stereo rig is unknown. If we make an exhaustive search for the epipolar geometry, the complexity is prohibitively high. The idea underlying our approach is to use a parallel fine-grain GA as an optimizer. Since the constraint on the rotation matrix is not fully exploited in the analytical method, nonlinear minimization can be used to improve the result.

关键词： parallel genetic algorithms butter-fly network epipolar geometry fine-grain GA position estimation fundamental matrix

来源：评论

学校读者我要写书评

暂无评论

Learning the optimum as a Nash equilibrium

引用

JOURNAL OF ECONOMIC DYNAMICS & CONTROL 2000年第4期24卷 483-499页

作者： Özyildirim, S Alemdar, NM Bilkent Univ Dept Econ TR-06533 Ankara Turkey Bilkent Univ Dept Management TR-06533 Ankara Turkey

This paper shows the computational benefits of a game theoretic approach to optimization of high dimensional control problems. A dynamic noncooperative game framework is adopted to partition the control space and to search the optimum as the equilibrium of a k-person dynamic game played by k-parallel genetic algorithms. When there are multiple inputs, we delegate control authority over a set of control variables exclusively to one player so that k artificially intelligent players explore and communicate to learn the global optimum as the Nash equilibrium. In the case of a single input, each player's decision authority becomes active on exclusive sets of dates-so that k GAs construct the optimal control trajectory as the equilibrium of evolving best-to-date responses. Sample problems are provided to demonstrate the gains in computational speed and accuracy. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： learning optimal control Nash equilibrium parallel genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Master-slave PGA and performance metrics

Master-slave PGA and performance metrics

引用

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

This work deals with a global model of PGA. A short overview of the model is introduced. Our research is concentrated on a master-slave algorithm (somewhere also called global). The complexity analysis of the algorithm is created and the processor optimality is derived from the analysis. Based on the optimality derivation, theoretical results with results from the real implementation are compared and the limitations of the algorithm are stated.

关键词： parallel genetic algorithms master-slave metrics

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：