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检索条件"主题词=EVOLUTIONARY PROGRAMMING"

共 651 条记录，以下是161-170 订阅

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Application of multi objective evolutionary programming to combined economic emission dispatch problem

Application of multi objective evolutionary programming to c...

引用

International Joint Conference on Neural Networks

作者： Jeyakumar, D. N. Venkatesh, P. Lee, Kwang. Y. PSNA Coll Engn Dept Elect & Elect Engn Dindigul India Univ Penn Dept Elect Engn University Pk PA 16802 USA

ISBN: (纸本)9781424413799

This paper describes a new Multi-Objective evolutionary programming (MOEP) method to solve the Combined Economic Emission Dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using weighted sum method. Hence the MOEP is proposed by employing the non-dominated solution ranking as selection mechanism for the bio-objective CEED problem. The developed algorithm is tested for a three-unit and a six-unit system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto optimal solutions of the multi-objective CEED problem in a single run.

关键词： combined economic emission dispatch evolutionary programming non-dominated solution Pareto optimal solutions.

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Self-Adaptive Mutation Strategy for evolutionary programming Based on Fitness Tracking Scheme

Self-Adaptive Mutation Strategy for Evolutionary Programming...

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IEEE Congress on evolutionary Computation

作者： Anik, Md. Tanvir Alam Ahmed, Saif Islam, K. M. Rakibul Bangladesh Univ Engn & Technol Dept Comp Sci & Engn CSE Dhaka 1000 Bangladesh

ISBN: (纸本)9781479904549;9781479904532

In order to achieve a satisfactory optimization performance by evolutionary programming (EP), it is necessary to ensure proper balance between global exploration and local exploitation. It is obvious that one single mutation operator is not the answer. Moreover, early loss of genetic diversity causes premature trapping around locally optimal points of the fitness landscape. This paper presents a fitness tracking based evolutionary programming (FTEP) algorithm incorporating a fitness tracking scheme to find the locally trapped individuals and treat them in a different way so that they are able to improve their performance. In comparison with other EP based algorithms, FTEP incorporates several mutation operators in one algorithm and employs a self-adaptive strategy to gradually self-adapt the mutation operators in order to apply an appropriate mutation operator on the individual based on its need. A test-suite of 25 benchmark functions has been used to evaluate the performance and results have been compared with some recent evolutionary systems. The experimental results show that FTEP often performs better than most other algorithms on most of the problems.

关键词： evolutionary programming mutation stagnant population fitness tracking

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Modified sampling frequency-sensitive network based on evolutionary programming for pattern clustering

Modified sampling frequency-sensitive network based on evolu...

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Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation III

作者： Hong, L Yulong, M Shanghai Univ Sch Commun & Informat Engn Shanghai 201800 Peoples R China

ISBN: (纸本)0819437654

In this paper, a modified evolutionary programming-based sampling frequency-sensitive network is proposed for pattern clustering. Many researchers study the neural networks for pattern clustering recently. The Kohonen Feature Maps (KFM) network and BP neural network are examples. But there are some problems with these models. For example, the network has a complicated structure and large amount of neurons. The neural network usually gets in unexpected local optimal solution. The results of pattern classification often correlate with the initial conditions. The fixed neural network structure is the major disadvantage for pattern clustering where the optimal number of patterns is unknown. Willie Chang presented a sampling frequency-sensitive network in 1997. The model has the advantages of simple structure and simple learning rules. But it also has fixed architecture. The algorithm is proposed in this paper which effectively uses the sampling frequency-sensitive network and the powerful parallel search optimization tool EP (evolutionary programming) which is presented by Fogel,D.B.. The modified Hubert index and cluster splitting and merging algorithm are used in network architecture evolution. The rule of minimum mean square error is used to get the optimal parameters. The proposed method has an advantage of that the optimal solution of neural network architecture and parameters can be get simultaneously. So the classification network can get the optimal number of clusters and the optimal vector quantization. The results of the experiment are given to prove that the neural network architecture can be changed for real world problems and get the optimal results.

关键词： neural network evolutionary programming vector quantization clustering

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Heuristic Search Strategy of evolutionary programming

Heuristic Search Strategy of Evolutionary Programming

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Han, Zhi-Ming Liu, Xian-Ping Tang, Miao Changchun Univ Technol Inst Informat & Spreading Engn Changchun 130012 Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China Changchun Univ Technol Coll Software Changchun 130012 Peoples R China

ISBN: (纸本)9781424451944

evolutionary programming (EP) has been successfully applied to many optimization problems in recent years. However, most experimental results of EP have been obtained under low-dimension condition and weren't perfect under high-dimension condition. A new evolutionary programming method which named Heuristic Search Strategy (HSS) was proposed for solving high-dimension optimization problem. It can grasp the information of distribution-status of population by control four parameters of population in the evolution process and adjust the mutation size of individual according to such information. HSS was test by using benchmark functions, the experimental results show that performance of HSS is better than other EP method obviously under high dimension condition.

关键词： evolutionary programming Heuristic Search Strategy optimization

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Using evolutionary programming for reconstruction of an irregularly sampled bandlimited sequence

Using evolutionary programming for reconstruction of an irre...

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4th International Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation

作者： Kalluri, C Rao, SS Rao, NS Villanova Univ Dept Elect & Comp Engn Villanova PA 19085 USA

ISBN: (纸本)0819441937

The problem of reconstructing an irregularly sampled discrete-time band-limited signal with unknown sampling locations can be analyzed using both geometric and algebraic approaches. This problem can be solved using iterative and non-iterative techniques including the cyclic coordinate approach and the random search method. When the spectrum of the given signal is band-limited to 'L' coefficients, the algebraic structure underlying the signal can be dealt using subspace techniques and a method is suggested to classify the solutions based on this approach. We numerically solve the Irregular Sampling at Unknown Locations (ISUL) problem by considering it as a combinatorial optimization problem. The exhaustive search method to determine the optimum solution is computationally intensive. The need for a more efficient optimization technique to save computational complexity leads us to propose evolutionary programming as a stochastic optimization technique. evolutionary algorithms, based on the models of natural evolution were originally developed as a method to evolve finite-state machines for solving time series prediction tasks and were later extended to parameter optimization problems. The solution space is modeled as a population of individuals, and the search for the optimum solution is obtained by evolving to the best individual in the population. We propose an evolutionary programming (EP) based method to converge to the global optimum and obtain the set of sampling locations for the given irregularly sampled signal. The results obtained by EP are compared with the Random Search and Cyclic Coordinate descent algorithms.

关键词： irregular sampling band limited evolutionary programming stochastic optimization random search cyclic coordinate unknown locations

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A new Heuristic evolutionary programming and its application in solution of the optimal power flow - Part I: Primary principle of Heuristic evolutionary programming

A new Heuristic Evolutionary Programming and its application...

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1998 International Conference on Power System Technology (POWERCON 98)

作者： Shi, LB Xu, GY Hua, ZM Chongqing Univ Dept Elect Engn Chongqing 400044 Peoples R China

ISBN: (纸本)0780347544

A new global optimization method called Heuristic evolutionary programming ( HEP ) is developed in this paper, based on the combination of the General evolutionary programming ( GEP ) and the classical gradient search techniques. This algorithm includes an important procedure called searching region change reduction. The developed algorithm can effectively reduce the CPU time with the global optimum's being obtained. Its efficiency is higher than that of GEP. Numerical results demonstrate the validity and effectiveness.

关键词： evolutionary programming gradient search techniques

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Improved evolutionary programming and Its Application in Navigation System

Improved Evolutionary Programming and Its Application in Nav...

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4th International Symposium on Intelligence Computation and Applications

作者： Liu, Jianjuan Henan Univ Technol Dept Elect Engn Zhengzhou 450007 Peoples R China

ISBN: (纸本)9783642049613

In designing BP neural network, it is difficult to determine the network parameters and weights, or to achieve the best effect under random perturbation, and impossible to satisfy the real-time requirement of system. To solve these problems, this paper introduces an evolutionary neural network and puts forward some improvements on conventional evolutionary programming. This algorithm is then used in the integrated navigation system. Experiment results indicate that BP neural network based on evolutionary programming can not only overcome the shortcomings of BP artificial neural network, but also avoid problems of genetic algorithms caused by binary-coded and cross operation. Also the navigation system based on this algorithm can get rid of the problems of kalman filter when the outside observation data are unreliable. Finally, the normal operation of kalman filter is ensured with a higher accuracy.

关键词： evolutionary programming evolutionary neural network inertial navigation kalman filter niche technology

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A sequential evolutionary programming approach to profit-based unit commitment

A sequential evolutionary programming approach to profit-bas...

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IEEE/PES Transmission and Distribution Conference and Exposition Latin America

作者： Contreras-Hernandez, E. J. Cedeno-Maldonado, J. R. Univ Puerto Rico Dept Elect & Comp Engn Mayaguez PR 00681 USA

ISBN: (纸本)9781424402878

This paper presents the application of a Sequential evolutionary programming (SEP) approach for solving the Profit-Based Unit Commitment problem (PBUC). The PBUC problem is a variant of the traditional Unit Commitment (UC) that has arisen as a result of the deregulation of power system markets. Specifically, PBUC is used for Generation Companies (Genco's) in order to maximize their own profits without the responsibility of satisfying necessary the forecasted demand. The PBUC is a highly dimensional mixed-integer optimization problem, which might be very difficult to solve. The SEP approach introduced in this paper offers a good balance between accuracy and computational effort while solving the PBUC problem. For its implementation, the PBUC problem is decomposed into three sub-problems that are solved in a sequential way. The proposed method is suitable for a wide variety of power system market rules. In this paper, two case studies are considered, each with different sets of power system market rules. The obtained results were compared with those available in the literature, and they showed the effectiveness of the proposed method for reaching optimal PBUC solutions.

关键词： energy markets evolutionary programming mixed-integer variables optimization problem profit-based unit commitment reserve markets

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Design for Self-Organizing Fuzzy Neural Networks Based on Adaptive evolutionary programming

Design for Self-Organizing Fuzzy Neural Networks Based on Ad...

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2nd IEEE International Conference on Advanced Computer Control

作者： Liu Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781424458479

A novel hybrid learning algorithm based on a evolutionary programming to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on evolutionary programming, to implement Takagi-Sugeno(TS) type fuzzy models is proposed in this paper. construct and parameters of the fuzzy neural network is trained by evolutionary algorithms. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive comparisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.

关键词： Fuzzy Neural Networks evolutionary programming fuzzy rule

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Scheduling in High Level Synthesis using Discrete evolutionary programming

Scheduling in High Level Synthesis using Discrete Evolutiona...

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3rd International Conference on Computing Communication and Networking Technologies (ICCCNT)

作者： Shilpa, K. C. Lakshminarayana, C. Singh, Manoj Kumar Dr AIT Bangalore Karnataka India BMSCE Bangalore Karnataka India Manuro Tech Res Bangalore Karnataka India

ISBN: (纸本)9781467356930

Scheduling is very important and critical part of high level synthesis. Quality of schedule rules the performance of chip in terms of cost and speed. Define Optimal schedule is a challenging and tedious task. This paper has pro posed the concept of Integer evolutionary programming (IEP) which is extension and discrete version of Evolution programming (EP) to handle the scheduling as a constraint optimization problem over the Integer Linear programming (ILP) formulation of problem. Proposed method can apply over any complexity of problem easily and efficiently. Verification of developed algorithm has given over benchmark problem.

关键词： High Level Synthesis Scheduling evolutionary programming ILP Constraint optimization

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