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检索条件"主题词=Multiobjective Genetic algorithm"

共 150 条记录，以下是101-110 订阅

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Gene Expression Rule Discovery with a Multi-objective Neural-genetic Hybrid

Gene Expression Rule Discovery with a Multi-objective Neural...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keedwell, Ed Narayanan, Ajit Univ Exeter Sch Engn Comp & Math Harrison BldgNorth Pk Rd Exeter EX4 4QF Devon England Auckland Univ Technol Sch Comp & Math Sci Auckland 1142 New Zealand

ISBN: (纸本)9781424483075

Recent advances in microarray technology allow an unprecedented view of the biochemical mechanisms contained within a cell. Deriving useful information from the data is still proving to be a difficult task. In this paper a novel method based on a multi-objective genetic algorithm that discovers relevant sets of genes and uses a neural network to create rules using the evolved genes is described. This hybrid method is shown to work on four well-established gene expression datasets taken from the literature. The results indicate that the approach can return biologically intelligible as well as plausible results. The proposed method requires no pre-filtering or preselection of genes.

关键词： biological techniques biology computing cellular biochemical mechanisms data mining evolved genes gene expression datasets gene expression rule discovery genetic algorithms genetics microarray technology multiobjective genetic algorithm multiobjective neu

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Survivable and Impairment-Aware Virtual Topologies for Reconfigurable Optical Networks: a Cognitive Approach

Survivable and Impairment-Aware Virtual Topologies for Recon...

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4th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT)

作者： Fernandez, N. Duran, R. J. de Miguel, I. Merayo, N. Aguado, J. C. Fernandez, P. Jimenez, T. Rodriguez, I. Sanchez, D. Lorenzo, R. M. Abril, E. J. Angelou, M. Tomkos, I. Univ Valladolid ETSI Telecomunicac Paseo Belen 15 E-47011 Valladolid Spain Athens Informat Technol Ctr Athens 19002 Greece

ISBN: (纸本)9781467320153;9781467320160

In this paper, we present a new version of a multiobjective genetic algorithm to design virtual topologies with the aim of reducing both the energy consumption and the network congestion. Moreover, we also propose an improved version of that algorithm, by including two cognitive techniques. Unlike the original version, the new proposals design survivable virtual topologies, as they provide shared path protection thus offering fault-tolerance capability with efficient resource utilization. Moreover, the proposed methods also take into account physical impairments in order to ensure that the survivable virtual topologies fulfill Quality of Transmission requirements in any scenario without failures or with a single cable failure. Finally, a simulation study demonstrates the advantages of the use of cognition.

关键词： Cognition failure protection multiobjective genetic algorithm power consumption QoT requirements virtual topologies

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Energy-Aware Partitioning for On-Chip Bus Architecture using a Multi-Objective genetic algorithm

Energy-Aware Partitioning for On-Chip Bus Architecture using...

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International Symposium on VLSI Design, Automation and Test (VLSI-DAT)

作者： Chiou, Lih-Yih Chen, Yi-Siou Jian, Ya-Lun Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

ISBN: (纸本)9781424484997

Incorporating power management during partitioning at the system level contributes considerably to energy efficient architecture. Designers commonly implement systems as a mix of partitioning blocks of various sizes, connected using bus interconnection architecture. Therefore, the use of a partitioning approach that partitions a system with the greatest possible idle time on a dedicated interconnection architecture has become unpractical for power management development. This work presents a novel energy-aware hardware clustering algorithm with a performance estimator for on-chip bus based architectures during high level synthesis, to enhance the quality of solutions for implementing power management systems. Experimental results obtained in four cases reveal that the proposed strategy generates a wide range of cost-effective solutions and is highly effective for today's hardware systems.

关键词： algorithm design and analysis Clustering algorithms Computer architecture Energy consumption genetic algorithms Partitioning algorithms Power demand energy efficient architecture energy-aware hardware clustering energy-aware partitioning genetic algorithms high level synthesis high level synthesis integrated circuit interconnections interconnection architecture multiobjective genetic algorithm on-chip bus architecture on-chip bus based architecture power management systems

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Selecting Syntactic Attributes for Authorship Attribution

Selecting Syntactic Attributes for Authorship Attribution

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International Joint Conference on Neural Networks (IJCNN)

作者： Varela, Paulo Justino, Edson Oliveira, Luiz S. PUCPR Curitiba Parana Brazil UFPR Dept Informat Curitiba Parana Brazil

ISBN: (纸本)9781424496365

In this work we present a methodology to select syntactic attributes for authorship attribution. The approach takes into account a multi-objective genetic algorithm and a Support Vector Machine classifier and it operates in a wrapper mode. Through a series of comprehensive experiments on a database composed of 3000 short articles written in Portuguese we show that the proposed methodology is able to provide a concise subset of attributes, which increases the recognition rate in about 15 percentage points.

关键词： Databases Error analysis Feature extraction genetic algorithms Support vector machines Syntactics attribute subset authorisation authorship attribution database management system genetic algorithms multiobjective genetic algorithm pattern classification recognition rate support vector machine classifier support vector machines syntactic attribute

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Optimization of a stacked microchannel heat sink using nanofluids (AL₂O₃-H₂O) with multiobjective optimization of thermal resistance and pressure drop 5

Optimization of a stacked microchannel heat sink using nanof...

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5th IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)

作者： Zaidan, Husain Ahmad, Robiah Ghazali, Normah Mohd Middle Tech Univ Inst Technol Baghdad Iraq Univ Teknol Malaysia Razak Fac Technol & Informat Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia Univ Teknol Malaysia Fac Mech Engn Johor Baharu 81310 Johor Malaysia

ISBN: (纸本)9781538662885

The single layer microchannel heat sink (MCHS) introduced a decade ago in microelectronics systems (MEMS) may no longer be an adequate solution. Possible extension of the layer of microchannels into a stacked system, by developing a multi-layer channel system, is investigated. The objectives of this study are to develop modified mathematical models for stacked (multi-layer) MCHS, using water and nanofluid of alumina-water based (Al2O3-H2O), at volumetric fraction phi of 1%, 2.5%, and 5%, for channel height of 287 mu m, finally, to optimize the thermal and hydrodynamic performance of the proposed model. The results of the optimization were carried out by multiobjective genetic algorithm, MOGA. The optimized thermal resistant and corresponding pressure drop, showed that at a channel height of 287 mu m and at volume fraction phi =5% water based alumina nanofluid provided the lowest value of the thermal resistance of 0.0282 /W for a multi-stacked (five layers) MCHS with pressure drop of 58.9 kPa as compared to 0.0867 C/W and 261.8 kPa for a single-layer MCHS at an optimum value for a (channel height to channel width) and beta (wall width to channel width) of 6.5 and 0.99 respectively.

关键词： Micro-channel heat sink MCHS thermal resistance optimization multiobjective genetic algorithm MOGA

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Community Detection in Multidimensional Networks 26

Community Detection in Multidimensional Networks

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Amelio, Alessia Pizzuti, Clara Natl Res Council Italy CNR Inst High Perf Comp & Net ICAR I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781479965731

The paper proposes a new approach to detect shared community structure in multidimensional networks based on the combination of multiobjective genetic algorithms, local search, and the concept of temporal smoothness, coming from evolutionary clustering. A multidimensional network is clustered by running on each slice a multiobjective genetic algorithm that maximizes the modularity on such a slice and, at the same time, minimizes the difference between the community structure obtained for the current layer and that found on the already considered dimensions. Experiments on synthetic and real-world datasets show the ability of the approach in discovering latent shared clustering of objects.

关键词： multidimensional networks social networks community detection evolutionary computation multiobjective genetic algorithm

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Pareto Optimization of Wideband Circular Ring Arrays

Pareto Optimization of Wideband Circular Ring Arrays

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2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Bianchi, Davide Genovesi, Simone Corucci, Alessandro Monorchio, Agostino Univ Pisa Dept Informat Engn Microwave & Radiat Lab I-56122 Pisa Italy

ISBN: (纸本)9781424449682

Our aim is to synthesize a wideband planar array which have to fulfill constraints imposed to the radiation pattern in terms of side lobe level (SLL) within a specified angular range. More in detail, we focus on a particular 2D configuration of antennas, namely the class of Concentric Ring Arrays (CRAs). Planar arrays present advantages with respect to linear ones in case of three dimensional beamforming and CRAs are also symmetric and guarantee a nearly invariant pattern for a full azimuthal coverage. We also require that the proposed solution is physically feasible, thus the minimum distance between the elements is fixed to be not lower than the dimensions of antenna (being this latter dictated by its physical size and by the need of mitigating the effect of e.m. coupling). Since we are interested in developing an efficient code for more than two objectives a dedicated version of a multi-objective genetic algorithm, called Nondominated Sorting genetic algorithm (NSGA-II) has been implemented to optimize the array factor of CRAs at three different frequencies.

关键词： NSGA-II Pareto optimisation Pareto optimization antenna radiation pattern antenna radiation patterns broadband antennas concentric ring arrays genetic algorithms multiobjective genetic algorithm nondominated sorting genetic algorithm planar antenna arrays side lobe level sorting three dimensional beamforming wideband circular ring arrays wideband planar array synthesis

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An Analysis of Clustering Objectives for Feature Selection Applied to Encrypted Traffic Identification

An Analysis of Clustering Objectives for Feature Selection A...

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2010 IEEE World Congress on Computational Intelligence

作者： Bacquet, Carlos Zincir-Heywood, Nur A. Heywood, Malcolm I. Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

ISBN: (纸本)9781424481262

This work explores the use of clustering objectives in a Multi-Objective genetic algorithm (MOGA) for both, feature selection and cluster count optimization, under the application of flow based encrypted traffic identification. We first explore whether it is possible to achieve the performance of a gold standard model (i.e., classification objectives), using a MOGA based on clustering objectives. Then, we explore the performance gain (if it exists) of applying a logarithmic transformation to the data prior to running the MOGA. Results show that MOGA trained with clustering objectives can closely reproduce the behavior of a gold standard model, not only in terms of the selected features, but also in terms of the achieved detection rate and false positives rate, above 90% and less than 1% respectively. On the other hand, no gain was observed by applying logarithmic transformation to the data.

关键词： cluster count optimization clustering objective analysis cryptography feature selection flow generation genetic algorithms gold standard model logarithmic transformation multiobjective genetic algorithm pattern clustering telecommunication security telecommunication traffic traffic identification encryption

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A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration

A fast optimization method of using nondominated sorting gen...

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IEEE International Conference on Granular Computing

作者： Liu, Y Zhou, C Ye, WJ Univ Exeter Dept Engn Exeter EX4 4QF Devon England

ISBN: (纸本)0780390172

Practical experience with numerical model calibration suggests that no single objective is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. The multi-objective genetic algorithm (MOGA) is used as automatic calibration method for a wide range of numerical models. The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real-world engineering problems. The NSGA-II-1NN algorithm, an effective and efficient methodology to reduce the number of actual fitness evaluations for solving the multiple-objective global optimization problem, is presented in this paper. The test results for multi-objective calibration show that the proposed method only requires about 38 percent of actual fitness evaluations of the NSGA-II.

关键词： Pareto optimisation calibration genetic algorithms pattern classification 1-nearest neighbor classifier MOGA optimization process NSGA-II-1NN algorithm Pareto set automatic calibration method fitness evaluation multiobjective genetic algorithm multiple-o genetic algorithm (GA) Pareto optimisation Pattern recognition CALIBRATION METHODS optimization method health

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Feedback-Based Global Instruction Scheduling for GPGPU Applications 1

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12th International Conference on Computational Science and Its Applications (ICCSA)

作者： Timm, Constantin Goerlich, Markus Weichert, Frank Marwedel, Peter Mueller, Heinrich TU Dortmund Dortmund Germany

ISBN: (数字)9783642311253

ISBN: (纸本)9783642311246;9783642311253

In the face of the memory wall even in high bandwidth systems such as GPUs, an efficient handling of memory accesses and memory-related instructions is mandatory. Up to now, memory performance considerations were only made for GPGPU applications at source code level. This is not enough when optimizing an application towards high performance: The code has to be optimized at assembly level as well. Due to the spreading of GPGPU-capable hardware in smaller and smaller devices, the energy consumption of a program is - besides the performance - an important optimization goal. In this paper, a novel compiler optimization technique, called FALIS (Feedback-based and memory-Aware gLobal Instruction Scheduling), is presented based on global instruction scheduling and multi-objective genetic algorithms. The approach uses a profiling-based feedback in order to take the measured performance and energy consumption values inside a compiler into account. Profiling on the real hardware platform is important in order to consider the characteristics of the underlying hardware. FALIS increases runtime performance of a GPGPU application by up to 13.02% and decreases energy consumption by up to 10.23%.

关键词： Energy-Aware Systems Compilers GPGPU multiobjective genetic algorithm Profiling

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