Modal parameters such as natural frequencies and mode shapes are sensitive indicators of structural damage. However, they are not only sensitive to damage, but also to the environmental conditions such as, humidity, w...
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Modal parameters such as natural frequencies and mode shapes are sensitive indicators of structural damage. However, they are not only sensitive to damage, but also to the environmental conditions such as, humidity, wind and most important, temperature. For civil engineering structures, modal changes produced by environmental conditions can be equivalent or greater than the ones produced by damage. This article proposes a damage detection method which is able to deal with temperature variations. The objective function correlates mode shapes and natural frequencies, and a parallelgenetic algorithm handles the inverse problem. The numerical model of the structure assumes that the elasticity modulus of the materials is temperature-dependent. The algorithm updates the temperature and damage parameters together. Therefore, it is possible to distinguish between temperature effects and real damage events. Simulated data of a three-span bridge and experimental one of the I-40 Bridge validate the proposed methodology. Results show that the proposed algorithm is able to assess the experimental damage despite of temperature variations.
We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of genetic Processors, is highly related to previously proposed ones su...
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We propose a computational model that is inspired by genetic operations over strings such as mutation and crossover. The model, Accepting Network of genetic Processors, is highly related to previously proposed ones such as Networks of Evolutionary Processors and Networks of Splicing Processors. These models are complete computational models inspired by DNA evolution and recombination. Here, we prove that the proposed model is computationally complete (it is equivalent to the Turing machine). Hence, it can accept any recursively enumerable language. In addition, we relate the proposed model with (parallel) geneticalgorithms or Evolutionary Programs and we set these techniques as decision problem solvers. (C) 2012 Elsevier B.V. All rights reserved.
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, explorin...
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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.
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 ...
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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.
In this paper, we present an efficient Hierarchical parallelgenetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (...
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In this paper, we present an efficient Hierarchical parallelgenetic Algorithm framework using Grid computing (GE-HPGA). The framework is developed using standard Grid technologies, and has two distinctive features: (1) an extended GridRPC API to conceal the high complexity of the Grid environment, and (2) a metascheduler for seamless resource discovery and selection. To assess the practicality of the framework, a theoretical analysis of the possible speed-up offered is presented. An empirical study on GE-HPGA using a benchmark problem and a realistic aerodynamic airfoil shape optimization problem for diverse Grid environments having different communication protocols, cluster sizes, processing nodes, at geographically disparate locations also indicates that the proposed GE-HPGA using Grid computing offers a credible framework for providing a significant speed-up to evolutionary design optimization in science and engineering. (c) 2006 Elsevier B.V. All rights reserved.
The job shop scheduling problem is one of the most important and complicated problems in machine scheduling. This problem is characterized as NP-hard. The high complexity of the problem makes it hard to find the optim...
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The job shop scheduling problem is one of the most important and complicated problems in machine scheduling. This problem is characterized as NP-hard. The high complexity of the problem makes it hard to find the optimal solution within reasonable time in most cases. Hence searching for approximate solutions in polynomial time instead of exact solutions at high cost is preferred for difficult instances of the problem. Meta-heuristic methods such as geneticalgorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. parallelizing the geneticalgorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel approach for the problem in which creating the initial population and parallelizing the genetic algorithm are carried out in an agent-based manner. Benchmark instances are used to investigate the performance of the proposed approach. The results show that this approach improves the efficiency. (C) 2010 Elsevier Ltd. All rights reserved.
This paper presents an Efficient Distributed genetic Algorithm for classification Rule extraction in data mining (EDGAR), which promotes a new method of data distribution in computer networks. This is done by spatial ...
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This paper presents an Efficient Distributed genetic Algorithm for classification Rule extraction in data mining (EDGAR), which promotes a new method of data distribution in computer networks. This is done by spatial partitioning of the population into several semi-isolated nodes, each evolving in parallel and possibly exploring different regions of the search space. The presented algorithm shows some advantages when compared with other distributed algorithms proposed in the specific literature. In this way, some results are presented showing significant learning rate speedup without compromising the accuracy. (C) 2010 Elsevier B.V. All rights reserved.
Parameter identification of pressure transient models accompanying cavitation and gas bubbles inside low hydraulic pipelines using parallel genetic algorithms(PGA)in a cluster of computers is presented. In this paper,...
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Parameter identification of pressure transient models accompanying cavitation and gas bubbles inside low hydraulic pipelines using parallel genetic algorithms(PGA)in a cluster of computers is presented. In this paper,pressure transient models are given to describe the flow behavior in a pipeline,and the numerical models of cavitation and gas bubbles are built to calculate the cavitation volume and gas bubble volume during the *** unknown parameters of the pressure transient mathematical models include the initial gas bubble volume,gas resolving time and gas releasing time *** identification of pressure transient mathematical models with geneticalgorithms(GA)on a single computer usually takes seven or eight days,which is out of *** execution time of GA becomes high due to time consuming of evaluating fitness *** order to predict the pressure transients and shorten the execution time of GA,MATLAB Distributed Computing toolbox and MATLAB geneticalgorithms toolbox are used to perform evaluation of fitness function at each generation by PGA,which is a parallel calculation on a cluster of *** on the minimization of the least-square errors between experimental data and simulation results,PGA is applied to search for global optimal model *** rate of computation time using PGA to using GA is acquired. Simulation results with identified parameters obtained by PGA are *** of simulation results with experimental data indicates that PGA is feasible to estimate unknown parameters in hydraulic low pressure pipeline transient model.
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 L...
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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.
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 g...
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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.
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