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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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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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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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parallel genetic Algorithm for VLSI Building Block Layout

Parallel Genetic Algorithm for VLSI Building Block Layout

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2nd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2006), vol.2

作者： Ning Xu Feng Huang Zhonghua Jiang Wuhan University of Science and Technology Wuhan China

The VLSI building block layout(BBL) becomes a more and more important problem for VLSI physical design. In this paper, A Multithread scheme for parallelizing a genetic algorithm for BBL placement optimization is presented. The parallel genetic algorithms(PGA) are realized, using sequence-pair(SP) as the representation. parallel algorithm can be used either to speed up a problem or to achieve a higher accuracy of solutions to a problem. Our experimental results on a SUN workstation with 4 CPUs have shown that the scheme is effective in improving performance of placement over that of a sequential implementation.

关键词： Building block layout parallel genetic algorithms Physical design

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On the behavior of parallel island models

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APPLIED SOFT COMPUTING 2023年 148卷

作者： da Silveira, Lucas A. de Lima, Thaynara A. de Barros, Jesse B. Soncco-Alvarez, Jose L. Llanos, Carlos H. Ayala-Rincon, Mauricio Univ Brasilia Dept Comp Sci Brasilia DF Brazil Univ Fed Goias Inst Math Goiania Go Brazil Univ Brasilia Dept Mech Engn Brasilia DF Brazil Univ Nacl San Antonio Abad Cusco Dept Informat Cuzco Peru Univ Brasilia Dept Math Brasilia DF Brazil

parallel island models (PIMs) are used to enhance the performance of evolutionary algorithms (EAs). In PIMs, each island executes an EA for evolving its local population, and periodically individuals migrate to neighborhoods synchronously or asynchronously. Neighborhoods are organized through a topology of communication, and migration policies guide individual exchange. This work explores migration policies over different communication topologies in synchronous and asynchronous PIMs to improve the speed-up and accuracy of genetic algorithms (GAs). The aim is to explain such models' adequacy from a general perspective, attempting to answer questions such as the best way to implement GAs in PIMs. To reach this goal, the quality of the solutions and the running time provided by proposed PIMs are evaluated over four .MP-hard problems of different combinatorial nature: reversal and translocation evolutionary distance, task mapping and scheduling, and N-Queens. The experiments show that tuning the parameters of the breeding cycle and migration policies is vital to guarantee that all proposed PIMs reach good speed-ups and more accurate solutions than the sequential GA. In addition, experiments ratify that synchronous models provide the best solutions, while asynchronous models deliver the best speed-ups. Finally, the results show that no model provides either better speed-up or accuracy in general since, as for sequential models, the nature of the problem define which would be the best-adapted PIM.

关键词： genetic algorithms parallel genetic algorithms parallel island models Migration policies

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parallelization of genetic algorithms for sorting permutations by reversals over biological data

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International Journal of Hybrid Intelligent Systems 2015年第1期12卷 53-64页

作者： Soncco-Álvarez, José Luis Almeida, Gabriel Marchesan Becker, Juergen Ayala-Rincón, Mauricio Departments of Computer Science University of Brasilia Brasilia Brazil Institute for Information Processing Technology Karlsruhe Institute of Technology Karlsruhe Germany Departments of Mathematics University of Brasilia Brasilia Brazil

Reversals are operations of great biological significance for the analysis of the evolutionary distance between organisms. Genome rearrangement through reversals, consists in finding the shortest sequence of reversals to transform one genome represented as a signed or unsigned permutation into another. When genes are non oriented and correspondingly permutations are unsigned, sorting by reversals came arise as a challenging problem in combinatorics of permutations. In fact, this problem is known to be NP-hard, but the question whether it is NP-complete remains open for more than twenty years. When permutations are signed and correspondingly genes are oriented, the problem is known to be in P. A parallelization of a standard GA (genetic Algorithm) is proposed for the problem of sorting unsigned permutations. This GA was previously reported in the literature as the most competitive regarding precision for which as control mechanism an 1.5-approximation algorithm was used. For the parallelization, the MPI Library of the C language was used and experiments were performed for calculating the execution time and precision. By increasing the number of individuals, experiment showed improvement in relation to previous approaches. Additionally, a virtualization of the GA using a MicroBlaze processor from Xilinx was performed on OVP for which the average number of executed instructions was of approximately 1.40 Giga instruction per second. In this extended version of this works originally presented in NaBIC 2013 biological data was generated and it was shown how the parallelization can be applied for their analysis. Specifically, the evolutionary distances between different pairs of organism were computed based on the set of non common genes in their mitochondrial DNA genome and the reversal distance between the sequences of common genes.

关键词： Sorting by reversals parallel genetic algorithms hardware virtualization

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parallel Evolutionary Approach to System Identification for Process Fault Diagnosis

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IFAC Proceedings Volumes 1998年第10期31卷 113-118页

作者： Teodor Marcu Paul M. Frank Gerhard Mercator Universität - GH-Duisburg FB9 FG Meβ- und Regelungstechnik Bismarckstraβe 81 (BB) D-47048 Duisburg GERMANY

The robustness issue in model-based diagnosis of process faults is addressed by means of parameter estimation. System identification is fonnulated as a problem of multiobjective optimization. The solution is based on parallel evolutionary algorithms of genetic type. Process coefficients are directly identified by a generalized on-line procedure. A simplification of the stage of symptom evaluation results. Application to a laboratory process is included. A diagnosis subsystem is designed to detect incipient faults in the components of a three-tank system.

关键词： fault diagnosis robustness process parameter estimation multiobjective optimization parallel genetic algorithms migration models three-tank system

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Graphics Processing Unit-Enhanced genetic algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

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EVOLUTIONARY BIOINFORMATICS 2018年第14期14卷 1176934318767889页

作者： Garcia-Calvo, Raul Guisado, J. L. Diaz-del-Rio, Fernando Cordoba, Antonio Jimenez-Morales, Francisco Univ Seville Dept Comp Architecture & Technol Seville Spain Univ Seville Dept Condensed Matter Phys Seville Spain

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequenti

关键词： Gene regulatory networks evolutionary computing parallel genetic algorithms GPU

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THE MIGRATION SCHEME BASED ON SCHEMA THEOREM OF PGAs

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Journal of Electronics(China) 2002年第3期19卷 315-319页

作者： GuanYu XuBaowen 等 DepartmentofComputerScienceandEngineering SoutheastUniversityNanjing210096 StateKeyLaboratoryofSoftwareEngineering WuhanUniversityWuhan430072

genetic algorithms (GAs) are efficient non-gradient stochastic search methods and parallel GAs (PGAs) are proposed to overcome the deficiencies of the sequential GAs, such as low speed, aptness to local convergence, etc. However, the tremendous increase in the communication costs accompanied with the parallelization stunts the further improvements of PGAs. This letter takes the decrease of the communication costs as the key to this problem and advances a new Migration Scheme based on Schema Theorem (MSST). MSST distills schemata from the populations and then proportionately disseminates them to other populations, which decreases the total communication cost among the populations and arms the multiple-population model with higher speed and better scalability.

关键词： parallel genetic algorithms Migration scheme Multiple-population model Complexity analysis

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Ultra-High-Speed WLAN Resource Allocation Based on Cloud Decision Algorithm

Ultra-High-Speed WLAN Resource Allocation Based on Cloud Dec...

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教育部中南地区高等学校电子电气基础课教学研究会第二十届学术年会

作者： Zu Yunxiao~1,Zhou Jie~1,Zeng Changchang~1,Wang Yuting~1,Song Yutian~2,Liu Xiao~2 (1.School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing,China, 100876.2.International School,Beijing University of Posts and Telecommunications,Beijing,China, 100876)

The cloud decision algorithm(CDA) for ultra-high-speed WLAN resource allocation is proposed. CDA is a combination of cloud computing system with parallel genetic *** are conducted to compare CDA with dynamic programming algorithm and simple genetic algorithm under cloud computing *** results show that the performance of CDA is better than that of the dynamic programming algorithm and the simple genetic *** transmission rate is increased markedly,the iteration number and computing time is declined *** can meet the real-time requirement of resource allocation in ultra-high-speed WLAN *** transmission rate of CDA with different nodes is also compared by *** results show that the transmission rate is the biggest with moderate nodes,and the transmission rate will be decreased with too many or too few nodes.

关键词： ultra-high-speed WLAN cloud decision algorithm cloud computing parallel genetic algorithms resource allocation

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