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检索条件"主题词=Evolution Algorithm"

共 124 条记录，以下是81-90 订阅

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A Cultural algorithm Based on Multilayer Belief Spaces and Its Application in Neural Network Fault Classifier

A Cultural Algorithm Based on Multilayer Belief Spaces and I...

引用

7th World Congress on Intelligent Control and Automation

作者： Huang, Halyan Liu, Mandan Gu, Xingsheng E China Univ Sci & Technol Dept Automat Shanghai 200237 Peoples R China

ISBN: (纸本)9781424421138

This paper presents a Cultural algorithm (CA) based on multilayer belief spaces that selects the best belief space from the multilayer belief spaces to guide the search of population space. The selected best belief space exploits knowledge extracted during the search to improve the performance of an evolutionary algorithm. We integrate cultural algorithm based on multilayer belief spaces and evolutionary programming to develop a more efficient algorithm, called CMAEP used for global optimization, and the knowledge sources in the belief spaces of the cultural algorithm are specifically designed. The tests of optimizing benchmark functions show the approach is valid and effective. Then we apply it to optimize the weights and thresholds of BP network as fault classifier to discriminate chemical process steady faults. The simulations on Tennessee Eastman process (TEP) show CMAEP can effectively escape from local optima to find the global optimal value comparing with other optimization methods and the optimized BP network based on CMAEP can obtain a satisfied diagnosis result. It is successful to apply the algorithm in Neural Network fault classifier.

关键词： evolution algorithm Cultural algorithm Multilayer Belief Spaces Neural Network

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Evolvable Hybrid Ensembles for Musical Genre Classification 22

Evolvable Hybrid Ensembles for Musical Genre Classification

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Genetic and evolutionary Computation Conference (GECCO)

作者： Kostrzewa, Daniel Ciszynski, Michal Brzeski, Robert Silesian Tech Univ Dept Appl Informat Gliwice Poland

ISBN: (纸本)9781450392686

The possibility of classifying musical songs according to their musical genre is becoming more challenging because of the millions of songs included in online databases. Therefore, reliable and efficient methods need to be developed that will automatically solve this task. In this article, the mentioned task is accomplished using sets of classifiers. The authors' contribution to the development of automatic musical genre recognition is using hybrid ensembles formed from deep neural networks and classical classifiers and the optimization process executed on the voting process of individual classifiers. Finally, differential evolution algorithms have been used to improve the classification quality further. The proposed evolutionary algorithm shows improvement in comparison with other optimization methods.

关键词： Ensembles classical classifiers deep neural networks music information retrieval optimization evolution algorithm

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Research on compound learning algorithm of Bayesian networks structures

Research on compound learning algorithm of Bayesian networks...

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6th International Symposium on Instrumentation and Control Technology

作者： Liu Xiao Li Haijun YanTai Univ Sch Comp Yantai 264006 Peoples R China Naval Aero Engn Acad Yantai 264001 Peoples R China

ISBN: (纸本)081946452X

Today, there are more mature and relative perfect means of how to learn structures or parameters from completed data and learn parameters of fixed structure from uncompleted data. But it is a more difficult thing that learning structures of Bayesian Networks from uncompleted data. A compound learning algorithm is proposed;it combines the EM algorithm, Monte Carlo sampling algorithm and evolution algorithm together, uses EM algorithm to learn parameters of networks in uncompleted data, then samples the best network, converts the uncompleted data to completed data, and then evolves the structure using evolution algorithm. This algorithm could get over the defect of EM algorithm that frequently gains local maximum. Because data processing is based on posterior networks structures, structures of Bayesian Networks is optimizing and optimizing with evolution computing, the reliability of complementary data is higher. Learning rate is high and performance of this algorithm is good.

关键词： Bayesian Networks structure learning EM algorithm Monte Carlo sampling algorithm evolution algorithm compound algorithm uncompleted data sets

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Discrete Monkey algorithm and Its Application in Transmission Network Expansion Planning

Discrete Monkey Algorithm and Its Application in Transmissio...

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IEEE-Power-and-Energy-Society General Meeting

作者： Wang, Jingran Yu, Yixin Zeng, Yuan Luan, Wenpeng Tianjin Univ Key Lab Power Syst Simulat & Control Tianjin 300072 Peoples R China BC Hydro & Power Author Burnaby V3N 4X8 BC Canada

ISBN: (纸本)9781424483570

Monkey algorithm (MA) is one of the evolution algorithms originally developed for optimization problems with continuous variables. In this paper, a discrete monkey algorithm (DMA) was proposed for transmission network expansion planning, one discrete optimization problem. It includes the representation of solution, the modification of objective function, climb process, watch-jump process, cooperation process, somersault process, stochastic perturbation mechanism and termination criteria. Large-step and small-step climb process are designed to avoid the disordered climb direction during the MA optimization process. Cooperation process and stochastic perturbation mechanism are also introduced to improve computational efficiency. The proposed method is applied to a 18-bus system and the IEEE 24-bus system. Numerical results demonstrate that DMA has powerful computational capability and is capable of solving different dimensions of expansion planning problems efficiently with small population size.

关键词： transmission network expansion planning discrete monkey algorithm optimization evolution algorithm

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Classifying Remote Sensing Image Using Gene Expression Programming algorithms

Classifying Remote Sensing Image Using Gene Expression Progr...

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9th International Conference on Natural Computation (ICNC)

作者： Liu, Mengwei Zeng, Guanghong Yuan, Guohui Pei, Yabo Yang, Zili Real Estate Surveying & Mapping Inst Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781467347143

Gene Expression programming (GEP) is a new algorithm of evolutionary computation, which possesses much more powerful abilities of parallel computation and global search to resolve complex problems. This paper constructs the GEP classification model and carries out experiments with Landsat Thematic Mapper(TM) image of Dongguan city in 1997. The result shows that the GEP classification model has good classification accuracy and demonstrates effectiveness in remote sensing image classification.

关键词： evolution algorithm Gene Expression Programming RS Image Classification

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Team Performance Prediction Based on an evolutionary algorithm 12

Team Performance Prediction Based on an Evolutionary Algorit...

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Li, Ni Huai, Wenqing Gong, Jing Li, Xiang Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China China Elect Standardizat Inst Ind Dev Res Ctr Beijing Peoples R China

ISBN: (纸本)9781467376822

In order to better adapt to complexity of environment and uncertainty of tasks, a team with high performances has become a key to improve productivity and achieve organizational success. However, team modeling is extremely complex, involving research on individual model and interaction between individuals. Based on using agents to build individual models, this paper proposed an organization cognitive behavior model as well as putting forward an interaction model based on interactive distances to quantitatively describe mutual influence between individuals. At the same time, an improved evolutionary algorithm is applied to deduce an organization's behavior and predict its performance. Finally, through analysis of simulation results, the proposed model is proved to be in line with actual situations.

关键词： evolution algorithm team performance individual model

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Concurrent Program Semantic Mutation Testing Based on Abstract Memory Model

Concurrent Program Semantic Mutation Testing Based on Abstra...

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IEEE International Conference on Information and Automation 2015

作者： Cao, Lixin Zheng, Wei Hu, Desheng Bai, Han Northwestern Polytech Univ Coll Software & Microelect Xian Shaanxi Provinc Peoples R China

ISBN: (纸本)9781467391047

Due to the fact that concurrent program's semantics can't be understood by relaxed memory model correctly, some unexpected faults, which are difficult to be detected, exist during its multi-threaded cross-execution. Therefore, this paper intends to establish an abstract memory model for concurrent program, conduct formal description of the memory access process that may exist in program memory access, and then utilize model checking technique to find out the common concurrent program error scenarios on this basis. We further design the semantic mutation operator, and investigate how to optimize the mutation process and how to generate mutants and test suite automatically. This study can set up a formal specification of concurrent program memory access semantics and provide a theoretical basis for other researches in the field of testing. It also serves as a basis to propose a semantic mutation testing technique oriented towards parallel program to assist parallel code testing in industry.

关键词： Software testing memory model semantic mutation testing symbol execution evolution algorithm

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A Dynamic evolutionary Scheduling algorithm for Cloud Tasks with High Efficiency and High QoS Satisfactions 14th

A Dynamic Evolutionary Scheduling Algorithm for Cloud Tasks ...

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14th CCF Conference on Computer Supported Cooperative Work and Social Computing

作者： Guo, Xiaoyong Zhou, Jiantao Inner Mongolia Univ Coll Comp Sci Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Hohhot Peoples R China

ISBN: (纸本)9789811513770;9789811513763

Efficient task scheduling is one of the main ways to increase cloud computing's throughput. In cloud computing, many tasks need to be scheduled on different virtual machines to augment system throughput while satisfying QoS conditions. Task scheduling is an NP-Complete problem, especially dynamic task scheduling in heterogenous cloud environments. This paper presents a dynamic task scheduling algorithm based on heterogeneous cloud environment. The proposed algorithm uses the preponderance of Topological sort, Genetic algorithm (GA) and NSGA-II. The experimental results show that the scheduling efficiency and QoS satisfaction of the algorithm are significantly better than GA, and the latter is one of the most commonly used heuristic optimization techniques in task scheduling problems.

关键词： Dynamic task scheduling evolution algorithm Task dependencies Global task priority Heterogenous cloud environment

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MULTIDISCIPLINARY DESIGN OPTIMIZATION WITH MULTIPLE DEGREES OF FREEDOM FOR AN AXIAL COMPRESSOR BASED ON DATA-DRIVEN 69

MULTIDISCIPLINARY DESIGN OPTIMIZATION WITH MULTIPLE DEGREES ...

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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Luo, Chuwei Chen, Jiang Liu, Yi Xiang, Hang Beihang Univ Beijing Peoples R China

ISBN: (纸本)9780791888087

Compressor design represents a multidisciplinary coupling problem encompassing aerodynamics, structure strength, vibration, fatigue, and acoustics. Multidisciplinary design optimization enables a comprehensive consideration of the interconnectedness among these disciplines, reasonably balances the conflicting requirements, and ultimately enhances the performance of products significantly while reducing the development cycle. In this paper, a multidisciplinary optimization system is established based on a data-driven multi-objective evolution algorithm. This algorithm is combined with the directly manipulated free-form deformation method to achieve multi-degree-of-freedom parameterized control. The research focuses on optimizing the aerodynamic and aeroelastic design of a 1.5-stage axial flow compressor in a gas turbine. Maximum efficiency and surge margin are set as the optimization objectives, while the constraint conditions involve flow rate, total pressure ratio, as well as the strength and resonance margin of the rotor blade. To improve the prediction accuracy of surge margin during optimization, a successive approximation method combined with efficiency-based residual convergence determination is utilized. The results show a significant reduction in the stress distribution of blades, with the maximum stress value decreased by 30.6%. Simultaneously, the surge margin of the compressor is increased by 3.36%, and the efficiency at the design point is also slightly improved. The optimization system can not only ensure optimization effectiveness, but also greatly reduce the design variables and evaluation time, which is effectively applicable to the multidisciplinary design optimization of compressors.

关键词： multidisciplinary design optimization data-driven evolution algorithm free-form deformation compressor

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Parameter Optimization for Main Driving Speed Control System of Temper Mill

Parameter Optimization for Main Driving Speed Control System...

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22nd Chinese Control and Decision Conference

作者： Li, Lanjun Wang, Xiangzhong Univ South China Sch Elect Engn Hengyang 421001 Peoples R China Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha 410014 Hunan Peoples R China

ISBN: (纸本)9781424451821

In main driving speed control system of temper mill, there are many adjustable parameters, their tuning ranges are wide, the parameters robustness are bad, and it is hard to find a set of control parameters which operate system under optimal condition with traditional parameter tuning method. In order to solve these problems, this paper puts forward an optimal setting method of control parameters based on evolution algorithm. The searching method with proper coding form, adaption function and other evolutionary operator is efficient and robust. The method is applied to parameter optimization of main driving speed control system of temper mill, and the system performance characteristics such as the most overshoot, response speed and accuracy turn out to be optimal.

关键词： Temper mill Parameter setting evolution algorithm Optimization

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