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5th International Conference on Intelligent Computing

作者： Ferraro, Marta B. Orendt, Anita M. Facelli, Julio C. Univ Utah Ctr High Performance Comp 155 S 1452 E Room 405 Salt Lake City UT 84112 USA Universidad de Buenos Facultad de Ciencias Exactas y Naturales Departamento de Fisica Buenos Aires DF Argentina Univ Utah Dept Biomed Informat Salt Lake City UT 84112 USA

ISBN: (纸本)9783642040696

This paper describes the application of our distributed computing framework for crystal structure prediction, Modified genetic algorithms for Crystal and Cluster Prediction (MGAC), to predict the crystal structure of the two known polymorphs of bicalutamide. The paper describes our success in finding the lower energy polymorph and the difficulties encountered in finding the second one. The results show that genetic algorithms are very effective in finding low energy crystal conformations, but unfortunately many of them are not plausible due to spurious effects introduced by the energy potential function used in the selection process. We propose to solve this by using a multi objective optimization GA approach, adding the unit cell volume as a second optimization target.

关键词： parallel genetic algorithms crystal structure prediction drug polymorphism

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parallel genetic algorithms for Optimization of Modular Neural Networks in Pattern Recognition

Parallel Genetic Algorithms for Optimization of Modular Neur...

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International Joint Conference on Neural Networks (IJCNN)

作者： Valdez, F. Melin, P. Parra, H. Tijuana Inst Technol Div Grad Studies & Res Mexico City DF Mexico Tijuana Inst Technol Mexico City DF Mexico

ISBN: (纸本)9781424496365

We described in this paper the use of Modular Neural Networks (MNN) for pattern recognition in parallel using a cluster of computers with a master-slave topology. In this paper, we are proposing the use of MNN to face recognition with large databases to validate this approach. Also, a parallel genetic algorithm to optimization architecture was used.

关键词： Computer architecture Computers Databases genetic algorithms MNN Multi-layer neural network Training computer clusters face recognition face recognition genetic algorithms master slave topology modular neural networks neural nets optimization architecture parallel genetic algorithms pattern recognition pattern recognition topology

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parallel genetic algorithms for the tuning of a fuzzy AQM controller

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International Conference on Computational Science and Its Applications (ICCSA 2003)

作者： Di Fatta, G Lo Re, G Urso, A CNR ICAR Ist Calcolo & Reti Ad Alte Prestazioni Palermo Italy

ISBN: (纸本)3540401555

This paper presents the results of the application of a parallel genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management,on Internet routers. The Active Queue Management. (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control police. Two different parallel implementations of the genetic algorithm axe adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.

关键词： parallel genetic algorithms fuzzy control active queue management

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Markov chain models of parallel genetic algorithms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2000年第3期4卷 216-226页

作者： Cantú-Paz, E Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Illinois Illinois Genet Algorithms Lab Urbana IL 61801 USA

Implementations of parallel genetic algorithms (GAs) with multiple populations are common, but they introduce several parameters whose effect on the quality of the search is not well understood. Parameters such as the number of populations, their size, the topology of communications, and the migration fate have to be set carefully to reach adequate solutions. This paper presents models that predict the effects of the parallel GA's parameters on its search quality, The paper reviews some recent results on the case where each population is connected to all the others and the migration rate is set to the maximum value possible. This bounding case is the simplest to analyze, and it introduces the methodology that is used in the remainder of the paper to analyze parallel GAs with arbitrary migration rates and communication topologies, This investigation considers that migration occurs only after each population converges: then, incoming individuals are incorporated into the populations and the algorithm restarts. The models find the probability that each population converges to the correct solution after each restart, and also calculate the long-run chance of success. The accuracy of the models is verified with experiments using one additively decomposable function.

关键词： communications topology Markov chains migration rate multiple populations parallel genetic algorithms

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Model-based spectral estimation of Doppler signals using parallel genetic algorithms

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2000年第1期19卷 75-89页

作者： González, JS Vázquez, KR Nocetti, DFG Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Dept Ingn Sistemas Computac & Automat Mexico City 01000 DF Mexico

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 genetic algorithms (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.

关键词： model-based spectral estimation parallel genetic algorithms real-time performance

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Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2008年第3期49卷 631-648页

作者： Choi, Jeoung-Nae Oh, Sung-Kwun Pedrycz, Witold Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G6 Canada Daelim Coll Dept Elect Engn Anyang 431715 Gyeonggi Do South Korea Univ Suwon Dept Elect Engn Hwaseong 445743 Gyeonggi Do South Korea Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

In this paper, we develop a design methodology for information granulation-based genetically optimized fuzzy inference system, which deals with the tuning method with a variant identification ratio for structural as well as parametric optimization of the reasoning system. The tuning is carried out with the aid of the hierarchical fair competition-based parallel genetic algorithms and it employs the mechanism of information granulation. This version of the genetic algorithm is a multi-population variant of parallel genetic algorithms, which is particularly suitable for handling multimodal problems of high-dimensionality. The granulation of information is realized with the aid of the C-Means clustering algorithm. The concept of information granulation is applied to the formation of the fuzzy inference system in order to realize its structural optimization. Here we divide the input space in order to construct the premise part of the fuzzy rules. Subsequently the consequence part of each fuzzy rule is organized based on the center points (prototypes) of data group obtained as a result of clustering. In particular, this concerns the fuzzy inference system-related parameters, i.e., the number of input variables to be used in the fuzzy inference system, a collection of a specific subset of input variables. the number of membership functions used for each input variable, and the polynomial type (order) occurring at the consequence part of fuzzy rules. Making use of a mechanism of simultaneous tuning for the parameters, we construct an optimized fuzzy inference system related to its structural as well as parametric optimization. A comparative analysis demonstrates that the proposed methodology leads to improved results when compared with some conventional methods exploited in fuzzy modeling. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Fuzzy inference system Information granulation parallel genetic algorithms Sequential and simultaneous tuning method Variant identification ratio Hierarchical fair competition-based parallel genetic algorithm C-Means clustering

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Modeling of hydraulic pipeline transients accompanied with cavitation and gas bubbles using parallel genetic algorithms

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JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME 2008年第4期75卷 041012-1-041012-8页

作者： Li, Songjing Yang, Chifu Jiang, Dan Harbin Inst Technol Dept Fluid Control & Automat Harbin 150001 Peoples R China

Mathematical models of pressure transients accompanied with cavitation and gas bubbles are studied in this paper to describe the flow behavior in a hydraulic pipeline. The reasonable prediction for pressure transients in a low pressure hydraulic pipeline largely depends on several unknown parameters involved in the mathematical models, including the initial gas bubble volumes in hydraulic oils, gas releasing and resolving time constants. In order to identify the parameters in the mathematical models and to shorten the computation time of the identification, a new method-parallel genetic algorithm (PGA)-is applied in this paper. Based on the least-square errors between the experimental data and simulation results, the fitness function of parallel genetic algorithms is programed and implemented. The global optimal parameters for hydraulic pipeline pressure transient models are obtained. The computation time of parallel genetic algorithms is much shorter than that of serial genetic algorithms. By using PGAs, the executing time is 20 h. However, it takes about 204 h by using GAs. Simulation results with identified parameters obtained by parallel genetic algorithms agree well with the experimental data. The comparison between simulation results and the experimental data indicates that parallel genetic algorithms are feasible and efficient to estimate the unknown parameters in hydraulic pipeline transient models accompanied with cavitation and gas bubbles.

关键词： pressure transients cavitation gas bubbles parallel genetic algorithms parameter identification

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Efficient hierarchical parallel genetic algorithms using grid computing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2007年第4期23卷 658-670页

作者： Lim, Dudy Ong, Yew-Soon Jin, Yaochu Sendhoff, Bernhard Lee, Bu-Sung Nanyang Technol Univ Sch Comp Engn Emerging Res Lab Singapore 639798 Singapore Honda Res Inst Europe GmbH D-63073 Offenbach Germany

In this paper, we present an efficient Hierarchical parallel genetic 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.

关键词： Grid computing parallel genetic algorithms

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Using Hadoop MapReduce for parallel genetic algorithms: A Comparison of the Global, Grid and Island Models

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EVOLUTIONARY COMPUTATION 2018年第4期26卷 535-567页

作者： Ferrucci, Filomena Salza, Pasquale Sarro, Federica Univ Salerno Dept Comp Sci Fisciano Italy UCL Dept Comp Sci London England

The need to improve the scalability of genetic algorithms (GAs) has motivated the research on parallel genetic algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs' computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store o

关键词： genetic algorithms parallel genetic algorithms Hadoop MapReduce global model grid model island model fault prediction

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Scalable parallel genetic algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2001年第2期16卷 153-168页

作者： Rivera, W Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA

genetic algorithms, 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, genetic algorithms 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 genetic algorithms 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.

关键词： coarse grained implementation cost efficiency fine grained implementation parallel genetic algorithms parallel systems scalability metrics

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