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检索条件"主题词=Multi-objective Genetic Algorithm"

共 724 条记录，以下是191-200 订阅

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Acoustic Emotion Recognition: Two Ways of Features Selection based on Self-Adaptive multi-objective genetic algorithm

Acoustic Emotion Recognition: Two Ways of Features Selection...

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International Conference on Informatics in Control, Automation and Robotics

作者： Christina Brester Maxim Sidorov Eugene Semenkin Institute of Computer Sciences and Telecommunication Siberian State Aerospace University Institute of Communications Engineering University of Ulm

ISBN: (纸本)9781479979202

In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.

关键词： Heuristic Feature Selection multi-objective genetic algorithm Self-Adaptation Probabilistic Neural Network Speech-based Emotion Recognition

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Improved H2/H∞ Control Based on multi-objective genetic algorithm

Improved H2/H∞ Control Based on Multi-Objective Genetic Alg...

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第二十四届中国控制会议

作者： Ma Qingliang, Hu Changhua (Teaching and Research Unit 302, Xi'an Research Institute of Hi-tech, Xi'an 710025, China)

A novel approach to H/Hoptimal control is presented based on multi-objective genetic algorithm （MOGA）. To design H/Hcontroller with less conservativeness, a kind of MOGA for H/Hcontrol （HHMOGA）is especially developed. HHMOGA takes the solutions of linear matrix inequality （LMI） method as initial population. Non-dominated sorting, niche, and elitist strategy are employed in order to ensure a better design. Simulation results show that HHMOGA can achieve better performances as compared with LMI method.

关键词： optimal control H2/H_∞ control multi-objective genetic algorithm

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Optimization of intermittent oil recovery mechanism based on multi-objective genetic algorithm

Optimization of intermittent oil recovery mechanism based on...

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第35届中国控制与决策会议

作者： Jingsong Wu Mingzheng Jiang Kangxing Dong Hukun Yang School of Mechanical Science and Engineering Northeast Petroleum University

With the continuous exploitation of oil fields,the problem of long-term inefficient operation of some pumping units is common in major oil fields in *** intermittent oil recovery mechanism can effectively avoid the wear and tear of empty pumping while reducing electrical energy *** Pareto multi-objective genetic algorithm is used to optimize the optimal downtime of the pumping units from the perspective of energy saving in the oil recovery system,to maximize the efficiency of oil recovery while minimizing power consumption.A comparison of the experimental results showed that the intermittent oil recovery mechanism was optimized to save 21.45% of energy consumption and improve the system efficiency by38.85%.This method solves the problems of empty pumping and inefficiency of pumping units and achieves the purpose of reducing the mechanical wear and tear of oil recovery machines,saving electrical energy,and improving the overall development benefit of the oil field.

关键词： pumping units intermittent pumping energy saving multi-objective genetic algorithm oil extraction efficiency

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CONSTRUCTION OF INTERPRETABLE AND PRECISE FUZZY MODELS USING FUZZY CLUSTERING AND multi-objective genetic algorithm

CONSTRUCTION OF INTERPRETABLE AND PRECISE FUZZY MODELS USING...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YUAN-LONG HOU ZONG-YI XING QIANG GAO YONG ZHANG LI-MIN JIA School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu 210094 Chi School of Transportation Beijing Jiaotong University Beijing 100044 China

An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed *** a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity.

关键词： Fuzzy modeling Fuzzy clustering multi-objective genetic algorithm TS fuzzy model Interpretability

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Springback control of sheet metal forming based on FEA and multi-objective genetic algorithm

Springback control of sheet metal forming based on FEA and m...

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第五届国际材料与热加工物理模拟及数值模拟学术会议

作者： Liu Wei* Yang Yuying Xing Zhongwen P. O. Box 435 School of Materials Science and Engineering Harbin Institute of Technology Harbin China 150001

In this paper, an automated optimization method by the integration of finite element analysis and optimization algorithm was presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, multi-objective genetic algorithm was applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. Furthermore, response surface model was employed to be a fast analysis tool to surrogate the time-consuming FEA procedure in the iterations of multi-objective genetic algorithm. An example was studied to illustrate the application of the approach proposed, and the authors came to the conclusion that the proposed method is more efficient than traditional manual FEA procedure and the ’trial and error’ approach for springback controlling.

关键词： springback sheet metal FEA multi-objective genetic algorithm

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multi objective genetic algorithm for Optimal Route Selection from a Set of Recommended Touristic Activities 24

Multi Objective Genetic Algorithm for Optimal Route Selectio...

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24th International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Orama, Jonathan Ayebakuro Moreno, Antonio Borras, Joan Ctr Tecnol Catalunya Eurecat Vila Seca Spain Univ Rovira & Virgili ITAKA Res Grp Tarragona Spain Ctr Tecnol Catalunya Eurecat C-Joanot Martorell15 Vila Seca 43480 Spain

ISBN: (纸本)9781643683263;9781643683270

It is a known fact that the order in which touristic activities are experienced plays a role in how enjoyable they are. This is the reason why tourists prefer to book carefully prepared day tours on arrival to a new destination, as they allow them to see the essence of the destination while traversing scenic routes. Tours are great, but they are expensive, do not allow room for personal exploration, and are built as a one-size-fits-all which does not consider the individual preferences of the tourist. In contrast, it is possible to make an optimal selection and ordering of touristic activities from a larger set of possibilities that match a tourist's personal preferences, balancing important aspects like diversity, spatial proximity, or degree of interest on popular places. We propose a multi-objective genetic algorithm that uses a weighted averaging operator to balance four diverse objective functions crafted to maintain diversity, proximity, interest on popularity, and cultural preference. The system has been evaluated against four baseline algorithms and found to perform significantly better for the specified purpose.

关键词： multi-objective genetic algorithm Travel Route Optimization Weighted Average objective Balancing

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Identification of Medicinal Plant by Fuzzy Local Binary Pattern and multi objective genetic algorithm 5

Identification of Medicinal Plant by Fuzzy Local Binary Patt...

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International Conference on Computer, Control, Informatics and its Applications (IC3INA)

作者： Laxmi, Gibtha Fitri Herdiyeni, Yeni Arkeman, Yandra Ibn Khaldun Univ KH Sholeh Iskandar St Km2 Bogor Indonesia Bogor Agr Univ Bogor Indonesia

ISBN: (纸本)9781538639788

This research proposes multi-objective genetic algorithm non-dominated-sorting (MOGA NSGA-II) of fuzzy local binary pattern to optimize LBP operator and fuzzy threshold for identification of Indonesian medicinal plants. multi-objective genetic algorithm (MOGA) is the genetic algorithm (GA) which is developed specifically for problems with multiple objectives. We evaluated 1,440 medicinal plant leaf images which belong to 30 species. The images were taken from Biofarmaka IPB, Cikabayan Farm, Greenhouse Center Ex-Situ Conservation of Medicinal Plant Indonesia Tropical Forest and Gunung Leutik. FLBP is used to handle uncertainty on images with various patterns. FLBP approach is based on the assumption that a local image neighbourhood may be characterized by more than a single binary pattern. The experimental results show that the correct selection of FLBP operator and threshold using MOGA can reach accuracy of 85%. It can be concluded that this propose method is capable to identify medicinal plants species efficiently and accurately.

关键词： Fuzzy Local Binary Pattern multi-objective genetic algorithm Texture Threshold

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Co-ordinated Design of AVR-PSS Using multi objective genetic algorithm

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1st International Conference on Swarm, Evolutionary, and Memetic Computing

作者： Selvabala, B. Devaraj, D. Sri Manakula Vinayagar Engn Coll Dept EEE Pondicherry India kalasalingam Univ krishnankoil Tamil Nadu India

ISBN: (纸本)9783642175626

Automatic Voltage Regulator (AVR) regulates the generator terminal voltage by controlling the amount of current supplied to the generator field winding by the exciter. Power system stabilizer (PSS) is installed with AVR to damp the low frequency oscillations in power system by providing a supplementary signal to the excitation system. Optimal tuning of AVR controller and PSS parameters is necessary for the satisfactory operation of the power system. When applying tuning method to obtain the optimal controller parameters individually, AVR improves the voltage regulation of the system and PSS improves the damping of the system. Simultaneous tuning of AVR and PSS is necessary to obtain better both voltage regulation and oscillation damping in the system. This paper deals with the optimal tuning of AVR controller and PSS parameters in the synchronous machine. The problem of obtaining the optimal controller parameters is formulated as an optimization problem and multi-objective genetic algorithm (MOGA) is applied to solve the optimization problem. The suitability of the proposed approach has been demonstrated through computer simulation in a Single Machine Infinite Bus (SMIB) system.

关键词： Automatic Voltage Regulator Power System Stabilizer multi-objective genetic algorithm Single Machine Infinite Bus system

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The performance analysis of a multi-objective immune genetic algorithm for flexible job shop scheduling

The performance analysis of a multi-objective immune genetic...

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PROLAMAT 2006 Conference on Knowledge Enterprise

作者： Wu, XiuLi Sun, ShuDong Niu, GangGang Zhai, YinNi Northwestern Polytech Univ Dept Mech & Elect Engn Xian 710072 Peoples R China

ISBN: (纸本)0387344020

First, a multi-objective immune genetic algorithm integrating immune algorithm and genetic algorithm for flexible job shop scheduling is designed. Second, Markov chain is used to analyze quantitatively its convergence. Third, a simulation experiment of the flexible job shop scheduling is carried out. Running results show that the proposed algorithm can converge to the Pareto frontier quickly and distribute evenly along the Pareto frontier.

关键词： flexible job shop scheduling multi-objective genetic algorithm convergence Markov chain

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Modular Granular Neural Networks Optimization with multi-objective Hierarchical genetic algorithm for human recognition based on iris biometric

Modular Granular Neural Networks Optimization with Multi-Obj...

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IEEE Congress on Evolutionary Computation

作者： Sanchez, Daniela Melin, Patricia Castillo, Oscar Valdez, Fevrier Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479904549;9781479904532

In this paper a new model of a multi-objective Hierarchical genetic algorithm (MOHGA) based on the Micro genetic algorithm (mu GA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed method can divide the data automatically into granules or sub modules, and chooses which data are for the training and which are for the testing phase. The proposed multi-objective genetic algorithm is responsible for determining the number of granules or sub modules and the percentage of data for training that can allow to have better results. The proposed method was applied to human recognition and its applicability with good results is shown, although the proposed method can be used in other applications such as time series prediction and classification.

关键词： Modular Neural Networks Hierarchical genetic algorithms multi-objective genetic algorithm Micro genetic algorithm Granular computing

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