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9th international Conference on Intelligent Systems Design and applications

作者： Muelas, Santiago LaTorre, Antonio Pena, Jose-Maria CeSViMa Parque Tecnol UPM Campus Montegancedo Madrid 28223 Spain Univ Politecn Madrid Fac Informat E-28660 Madrid Spain

ISBN: (纸本)9781424447350

Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the search methods is still an open issue. Finding an algorithm with competitive results in the range of 50 to 500 dimensions is a difficult achievement. This contribution explores the use of a hybrid memetic algorithm based on the differential evolution algorithm, named MDE-DC. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods, that separately obtain very competitive results in either low or high dimensional problems. This paper uses the benchmark problems and conditions required for the workshop on "evolutionary algorithms and other metaheuristics for Continuous optimization Problems - A Scalability Test" chaired by Francisco Herrera and Manuel Lozano.

关键词： memetic algorithms de continuos optimization

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Collision detection optimization in a multi-particle system

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international JOURNAL OF COMPUTATIONAL GEOMETRY & applications 2003年第4期13卷 279-301页

作者： Gavrilova, ML Rokne, J Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

Collision detection optimization in an event-driven simulation of a multi-particle system is one of the crucial tasks, determining the efficiency of the simulation. We present the event-driven simulation algorithm that employs dynamic computational geometry data structures as a tool for collision detection optimization (CDO). The first successful application of the dynamic generalized Voronoi diagram method for collision detection optimization in a system of moving particles is discussed. A comprehensive comparision of four kinetic data structures in d-dimensional space, performed in a framework of an event-driven simulation of a granular-type materials system, is supported by the experimental results.

关键词： optimization algorithms dynamic data structures multi-particle system Delaunay tessellation regular subdivision segment tree

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Optimal Feature Subset Selection for Fuzzy Extreme Learning Machine using Genetic Algorithm with multilevel Parameter optimization 5

Optimal Feature Subset Selection for Fuzzy Extreme Learning ...

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IEEE international Conference on Signal and Image Processing applications (ICSIPA)

作者： Kale, Archana Sonavane, Shefali Walchand Coll Engn Dept Comp Sci & Engn Sangli 416415 Maharashtra India Walchand Coll Engn Dept Informat Technol Sangli 416415 Maharashtra India

ISBN: (纸本)9781509055593

The crucial objective of this paper is to design a hybrid model of the genetic algorithm for fuzzy extreme learning machine classifier (GA-FELM), which selects an optimal feature subset by using the multilevel parameter optimization technique. Feature subset selection is an important task in pattern classification and knowledge discovery problems. The generalization performance of the system is not only depending on optimal features but also dependent upon the classifier (learning algorithm). Therefore, it is an important task to select a fast and efficient classifier. Research efforts have affirmed that extreme learning machine (ELM) has superior and accurate classification ability. However, ELM is failed to handle the uncertain data. One of the alternative solutions is fuzzy-ELM, which combines the advantages of fuzzy logic and ELM. GA-FELM is able to handle curse of dimensionality problem, optimization problem and weighted classification problem with maximizing classification accuracy by minimizing the number of features. In order to validate the efficiency of GA-FELM, the comparative performance is evaluated by using three different approaches viz. 1. ELM and GA-ELM 2. GA-ELM and GA-FELM 3. GA-FELM and GA-existing classifier. The result analysis shows that classification accuracy is improved with 9% while reducing 62% features.

关键词： Genetic algorithms Sociology Statistics Frequency selective surfaces optimization Search problems Neurons

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Music Segmentation With Genetic algorithms

Music Segmentation With Genetic Algorithms

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20th international Conference on Database and Expert Systems applications

作者： Rafael, Brigitte Oertl, Stefan Affenzeller, Michael Wagner, Stefan Re Compose GmbH Vienna Austria Upper Austria Univ Appl Sci Sch Informat Commun & Media Heurist & Evolut Algorithms Lab Hagenberg Austria

ISBN: (纸本)9780769537634

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the structure of a composition. Based on structural information, several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization can be further improved. Various approaches are available to achieve an appropriate segmentation of a given composition. The authors of this paper present an approach to appliy genetic algorithms for a solution to the segmentation problem.

关键词： music information retrieval music segmentation pattern recognition heuristic optimization genetic algorithms

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New space vector modulation technique for single-phase multilevel converters

New space vector modulation technique for single-phase multi...

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IEEE international Symposium on Industrial Electronics

作者： Leon, J. I. Portillo, R. Franquelo, Leopoldo G. Vazquez, S. Carrasco, J. M. Dominguez, E. Univ Seville Dept Elect Engn Seville Spain

ISBN: (纸本)9781424407545

Single-Phase multilevel converters are suitable for medium power applications as photovoltaic systems and switched reluctance machines. An overview of possible modulation methods including carrier-based Pulse Width Modulation and Space Vector Modulation techniques for multilevel single-phase converters is presented. A new space vector modulation for this type of converters is proposed. This space vector modulation method is very simple presenting low computational cost. Different solutions for the space vector modulation are presented achieving similar output results but imposing restrictions on the power converter topology. optimization algorithms balancing the DC-Link voltage or minimizing the commutation losses are presented. Experimental results using a 150 kVA five-level diode-clamped converter are shown to validate the proposed modulation and optimization methods.

关键词： Space vector modulation Converters commutation losses Photovoltaic systems multilayers optimization methods modulation mode phase convertors

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Query processing and optimization in temporal object-oriented databases

Query processing and optimization in temporal object-oriente...

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8th international workshop on Database and Expert Systems applications

作者： Wang, L Wing, M Davis, C Revell, N Middlesex Univ London United Kingdom

ISBN: (纸本)0818681489

This paper investigates temporal query processing and optimization in the context of object-oriented databases. Based on our temporal object data model and algebra, a strategy of decomposition is presented to process temporal queries that involve associations of both aggregation hierarchy and time-reference. That is, evaluation of an enhanced path, which is defined to extend a path with time-reference, is decomposed by initially dividing the path into two sub-paths: one contains the time-stamped class that can be optimized by making use of the ordering information of temporal data and another is an ordinary sub-path which can be further decomposed and evaluated using different algorithms. The intermediate results of traversed two sub-paths are then joined together to create the query output. algorithms have been implemented with stream processing techniques and presented with cost analysis. optimization issues have been discussed.

关键词： Relational database systems

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Debugging and optimization of HPC Programs with the Verrou Tool 3

Debugging and Optimization of HPC Programs with the Verrou T...

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IEEE/ACM 3rd international workshop on Software Correctness for HPC applications (Correctness)

作者： Fevotte, Francois Lathuiliere, Bruno EDF R&D Palaiseau France

ISBN: (纸本)9781728160153

The analysis of Floating-Point-related issues in HPC codes is becoming a topic of major interest: parallel computing and code optimization often break the reproducibility of numerical results across machines, compilers and even executions of the same program. This paper presents how the Verrou tool can help during all stages of the Floating-Point analysis of HPC codes: diagnose, debugging and optimization. Recent developments of Verrou are presented, along with examples illustrating the interest of these new features for industrial codes such as code aster. More specifically, the Verrou arithmetic back-ends now allow analyzing or emulating mixed-precision programs. Interlibm, an interposition layer for the mathematical library, is introduced to mitigate long-standing issues with algorithms from the libm. Finally, debugging algorithms are extended in order to produce useful information as soon as it is available. All these features are available in released version 2.1.0 and upcoming version 2.2.0.

关键词： Floating-point arithmetic Verrou mixed precision Verification & Validation

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Switching angle optimization based genetic algorithms for harmonic reduction in three-phase PWM inverters

Switching angle optimization based genetic algorithms for ha...

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2010 11th international workshop on Symbolic and Numerical Methods, Modeling and applications to Circuit Design, SM2ACD 2010

作者： Mahmood Al-Rawi, Osama Y. Noaman, Noaman M. Alkhalidy, Mohammed Majid Mohammed Electrical and Electronics Dept. Gulf University Sanad Bahrain

ISBN: (纸本)9781424468164

A simple but powerful design method based on real-coded Genetic algorithms (GAs) to solve the minimization of the Total Harmonic Distortion (THD) criterion is obtained. GAs provides a much simpler approach to off-line tuning of Pulse width Modulation (PWM) switching angles than the rather complicated non-genetic optimization algorithms. Genetic algorithms are exploratory search and optimization procedures that were devised on the principles of natural evolution and population genetics. ©2010 IEEE.

关键词： Genetic algorithms

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Auto-tuning for GPGPU applications using performance and energy model

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JOURNAL OF SYSTEMS ARCHITECTURE 2016年 62卷 40-53页

作者： Lin, Chih-Sheng Teng, Shih-Meng Hsiung, Pao-Ann Ind Technol Res Inst Informat & Commun Res Labs Hsinchu Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn Chaiyi Taiwan

The general-purpose graphic processing unit (GPGPU) is a popular accelerator for general applications such as scientific computing because the applications are massively parallel and the significant power of parallel computing inheriting from GPUs. However, distributing workload among the large number of cores as the execution configuration in a GPGPU is currently still a manual trial-and-error process. Programmers try out manually some configurations and might settle for a sub-optimal one leading to poor performance and/or high power consumption. This paper presents an auto-tuning approach for GPGPU applications with the performance and power models. First, a model-based analytic approach for estimating performance and power consumption of kernels is proposed. Second, an auto-tuning framework is proposed for automatically obtaining a near-optimal configuration for a kernel computation. In this work, we formulated that automatically finding an optimal configuration as the constraint optimization and solved it using either simulated annealing (SA) or genetic algorithm (GA). Experiment results show that the fidelity of the proposed models for performance and energy consumption are 0.86 and 0.89, respectively. Further, the optimization algorithms result in a normalized optimality offset of 0.94% and 0.79% for SA and GA, respectively. (C) 2015 Elsevier B.V. All rights reserved.

关键词： GPGPU Performance and Energy modeling Auto-tuning optimization

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A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning

A Scalability Test for Accelerated DE Using Generalized Oppo...

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9th international Conference on Intelligent Systems Design and applications

作者： Wang, Hui Wu, Zhijian Rahnamayan, Shahryar Kang, Lishan Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China Univ Ontario Inst Technol Fac Engn & Appl Sci Oshawa ON L1H 7K4 Canada

ISBN: (纸本)9781424447350

In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary algorithms and other Metaheuristics for Continuous optimization Problems - A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100, 200, and 500 in order to compare with the results of other algorithms which are participating in this workshop. Current work is based on opposition-based differential evolution (ODE) and our previous work, accelerated PSO by generalized OBL.

关键词： Particle swarm optimization (PSO)

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