In this paper, we present a system-level fault identification algorithm, using a parallelgenetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model ...
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ISBN:
(纸本)1932415610
In this paper, we present a system-level fault identification algorithm, using a parallelgenetic algorithm, for diagnosing faulty nodes in large heterogeneous systems. The algorithm is based on a probabilistic model where individual node fails with an a priori probability p. The assumptions concerning test outcomes are the same as in the PMC model, that is, fault-free testers always give correct test outcomes and faulty testers are totally unpredictable. The parallel diagnosis algorithm was implemented and simulated on randomly generated large systems. Simulations results are provided showing that the parallel diagnosis did improve the efficiency of the evolutionary diagnosis approach, in that it allowed faster diagnosis of faulty situation, making it a contribution to present techniques.
We use a parallel multi-objective genetic algorithm to drive a search and reconstruction spectroscopic analysis of plasma gradients in inertial confinement fusion (ICF) implosion cores. In previous work, we had shown ...
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ISBN:
(纸本)1595930108
We use a parallel multi-objective genetic algorithm to drive a search and reconstruction spectroscopic analysis of plasma gradients in inertial confinement fusion (ICF) implosion cores. In previous work, we had shown that our serial multi-objective genetic Algorithm was a good method to solve two-criteria X-ray spectroscopy diagnostics problems. However, this serial version was slow and we therefore could not incorporate better physics and more criteria to solve larger problems and handle larger data sets. In this paper, we develop and use a parallel multi-objective genetic algorithm based on a master-slave model to solve three criteria spectroscopic analysis problems. The algorithm works well in reconciling experimental observations with theoretical physics model parameters. In addition, theoretical analysis and experimental results on the parallelized version show good scalability with up to 150 processors. This reduces the time for running the GA from 9.6 hours to 5.9 minutes.
geneticalgorithms 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 p...
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ISBN:
(纸本)1595930108
geneticalgorithms 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.
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 Cl...
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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.
In this article, we propose two new adaptive line enhancement schemes for tracking the power frequency signal corrupted with broad-band noise. The first scheme named as Hybrid parallelgenetic Algorithm Based Line Enh...
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In this article, we propose two new adaptive line enhancement schemes for tracking the power frequency signal corrupted with broad-band noise. The first scheme named as Hybrid parallelgenetic Algorithm Based Line Enhancer (HPGABLE) employs parallelgenetic Algorithm (PGA) and Least Mean Square (LMS) Algorithm to obtain the optimal set of filter coefficients while the delay is selected on an ad hoc basis. In the second proposed Recursive Hybrid parallelgenetic Algorithm Based Line Enhancer (RHPGABLE) scheme the delay as well as the filter coefficients are estimated recursively. In the recursion of the proposed RHPGABLE algorithm, parallelgenetic Algorithm (PGA) based on coarse grained approach is employed for estimating the delay while the filter coefficients are estimated by PGA and LMS Algorithm. RHPGABLE is an unsupervised scheme in the sense that no a priori knowledge of delay, filter coefficients and the associated training signal component is assumed to be available. The proposed schemes could be successfully tested on both synthetic data as well as the data obtained from the Steel Melting Shop of Rourkela Steel Plant.
Recent breakthroughs in the mathematical estimation of parallelgenetic algorithm parameters are applied to the NP-complete problem of scheduling multiple tasks on a cluster of computers connected by a shared bus. Num...
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Recent breakthroughs in the mathematical estimation of parallelgenetic algorithm parameters are applied to the NP-complete problem of scheduling multiple tasks on a cluster of computers connected by a shared bus. Numerous adjustments to the original method of parameter estimation were made in order to accurately reflect differences in the problem model. The parallel scheduler used m-ary encoding and included a shared communication bus constraint. Fitness was an indirect computation requiring an evaluation of the meaning and implications (i.e., effect on communication time) of the encoding. The degree of correctness was defined as the "nearness" to the optimal schedule that could be obtained in a limited amount of time. Experiments reveal that the parallel scheduling algorithm developed very accurate schedules when the modified parameter guidelines were used. This article describes the scheduling problem, the parallelgenetic scheduler, the adjustments made to the mathematical estimations, the quality of the schedules that were obtained, and the accuracy of the schedules compared to mathematically predicted expected values. (C) 2004 Elsevier B.V. All rights reserved.
geneticalgorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, ...
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geneticalgorithms, search algorithms based on the genetic processes observed in natural evolution, have been used to solve difficult problems in many different disciplines. When applied to very large-scale problems, geneticalgorithms exhibit high computational cost and degradation of the quality of the solutions because of the increased complexity. One of the most relevant research trends in geneticalgorithms is the implementation of parallel genetic algorithms with the goal of obtaining quality of solutions efficiently. This paper first reviews the state-of-the-art in parallel genetic algorithms. parallelization strategies and emerging implementations are reviewed and relevant results are discussed. Second, this paper discusses important issues regarding scalability of parallel genetic algorithms.
The modelling of real-world processes such as air quality is generally a difficult task due to both their chaotic and non-linear phenomenon and high dimensional sample space. Despite neural networks (NN) have been use...
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The modelling of real-world processes such as air quality is generally a difficult task due to both their chaotic and non-linear phenomenon and high dimensional sample space. Despite neural networks (NN) have been used successfully in this domain, the selection of network architecture is still problematic and time consuming task when developing a model for practical situation. This paper presents a study where a parallelgenetic algorithm (GA) is used for selecting the inputs and designing the high-level architecture of a multi-layer perceptron model for forecasting hourly concentrations of nitrogen dioxide at a busy urban traffic station in Helsinki. In addition, the tuning of GA's parameters for the problem is considered in experimental way. The results showed that the GA is a capable tool for tackling the practical problems of neural network design. However, it was observed that the evaluation of NN models is a computationally expensive process, which set limits for the search techniques. (C) 2004 Elsevier Ltd. All rights reserved.
This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach...
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This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.
Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolu...
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Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using geneticalgorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods. (C) 2000 Elsevier Science B.V. All rights reserved.
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