In this paper, a improved self-adaptive evolutionary Algorithm is researched and adopted in CBR engine design system. Because attribute weight of CBR system can obviously influence the accuracy of case-search, the evo...
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In this paper, a improved self-adaptive evolutionary Algorithm is researched and adopted in CBR engine design system. Because attribute weight of CBR system can obviously influence the accuracy of case-search, the evolutionary programming Algorithm is used to get attribute weight for adapting to changes of requirement. This paper analyses the coding method of chromosome, educes the adaptability function based on reference case base REF and testing case base TEST, develops a new Self-Adaptive aberrance method that can suit the changes of adaptability function and overcome the lack of conventional evolutionary programming Algorithm. This paper also gives the Algorithm procedure of self-adaptive evolutionary programming and ensures self-adaptive evolutionary programming Algorithm possesses the furthest dispersive search ability during the solution space and the furthest accurate search ability during the local situation. Experiment results prove the validity of self-adaptive Algorithm and CBR design system is used successfully in engine design process.
A Bayesian network is a graphical model for probabilistic relationships among a set of variables. Over the last two decades, the Bayesian network has become a popular representation for encoding uncer
A Bayesian network is a graphical model for probabilistic relationships among a set of variables. Over the last two decades, the Bayesian network has become a popular representation for encoding uncer
This paper is concerned with the optimum proportional-integral-derivative (PID) controller design for a PM dc motor position control. Since the evolutionary programming (EP) algorithm has been considered as a useful t...
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This paper is concerned with the optimum proportional-integral-derivative (PID) controller design for a PM dc motor position control. Since the evolutionary programming (EP) algorithm has been considered as a useful technique for finding global optimization solutions for certain complicated functions in recent years. In this paper, we attempt to combine the EP algorithm with the PID control design to solve the positioning control problem of a PM dc motor, such that a performance index of integrated-absolute error (IAE) is minimized. Last, a SYL-5 PM dc motor position control system is used to verify the superiority of the proposed method. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.
In this paper, a approach for automatically generating fuzzy rules from sample patterns is ***, with Cauchy mute operator and Gaussian mute operator, we propose a new evolutionary programming(EP) based on self-adaptiv...
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In this paper, a approach for automatically generating fuzzy rules from sample patterns is ***, with Cauchy mute operator and Gaussian mute operator, we propose a new evolutionary programming(EP) based on self-adaptive ***, a self-adaptive fuzzy neural network is built based on the new evolutionary *** this method, structure identification and parameters estimation are performed automatically and *** simulation results show that the proposed method in this paper can produce the compact and high performance fuzzy rule-base in comparison with other algorithms.
evolutionary programming (EP) is a kind of stochastic optimization algorithm. The goal of EP is to achieve intelligent behavior through simulated evolution. EP algorithms are based on an arbitrarily initialized popula...
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ISBN:
(纸本)9781629939254
evolutionary programming (EP) is a kind of stochastic optimization algorithm. The goal of EP is to achieve intelligent behavior through simulated evolution. EP algorithms are based on an arbitrarily initialized population of search points which evolves towards better and better regions in the search space by means of randomized process of mutation and selection. To avoid premature convergence and balancing the ability of exploration and exploitation has become one of the important aspects of EP's study. We describe the classic evolutionary programming (CEP) which is the basic algorithm of evolutionary programming. FEP improved CEP by replacing the Gaussian mutation in CEP by Cauchy mutation. The main focus of this thesis is several EP algorithms which are introduced in detail and studied.
A new mutation operator based on the T probability distribution is studied. The T probability distribution is stable and can generate an offspring that is farther away from its parent than the commonly employed Gaussi...
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ISBN:
(纸本)9781424404759
A new mutation operator based on the T probability distribution is studied. The T probability distribution is stable and can generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Moreover, it has a better fine-tuning ability than the Cauchy mutation. In this paper, evolutionary programming (EP) with mutations based on the T probability distribution is studied. The new algorithm is tested on 23 benchmark functions and compared with the conventional EP and the fast EP. The experimental results demonstrate that the performance of the proposed algorithm outperforms the conventional EP and the fast EP.
This paper presents a new approach to economic dispatch (ED) problem with non-continuous and non-smooth cost functions using a hybrid evolutionary programming (EP) algorithm. In the proposed method the concept of mult...
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This paper presents a new approach to economic dispatch (ED) problem with non-continuous and non-smooth cost functions using a hybrid evolutionary programming (EP) algorithm. In the proposed method the concept of multi-agent (MA) systems and EP are integrated together to form a new multi-agent evolutionary programming (MAEP) approach. In MAEP, an agent represents a candidate solution to the optimization problem in hand, and all agents live together in a global environment. Each agent senses its local environment, competes with its neighbors, and also learns by using its own knowledge. MAEP uses these agent-agent interactions and the evolutionary mechanism of EP to obtain the optimal solution. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms in the literature.
This paper develops an efficient and reliable evolutionary-programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load demand specificatio...
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This paper develops an efficient and reliable evolutionary-programming-based algorithm for solving the environmentally constrained economic dispatch (ECED) problem. The algorithm can deal with load demand specifications in multiple intervals of the generation scheduling horizon. In the paper, the principal components of the evolutionary-programming-based ECED algorithm are presented. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a test system.
Wavelet based compression is mostly utilized to compress the videos. Most of the wavelet-based compression techniques first determines the coefficients and then performs a threshold-based operation to obtain the compr...
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ISBN:
(纸本)9781467313421
Wavelet based compression is mostly utilized to compress the videos. Most of the wavelet-based compression techniques first determines the coefficients and then performs a threshold-based operation to obtain the compressed video. In such works, there is a lack of analysis in selecting an appropriate threshold value. To overcome such drawback presented in the existing methods, we have proposed a video compression technique with an adaptive threshold selection method to compress the videos in this paper. The simulation results show the effectiveness of proposed video compression technique. The performance of the video compression technique is evaluated by comparing the result of proposed technique with the existing video compression technique. The comparison result shows that the enhanced quality of the image as well as the compression ratio of the proposed technique.
In this paper, a Improved evolutionary programming (IEP) is proposed to solve global numerical optimization problems with continuous variables. In the methodology, the well-known evolutionary programming (EP) is used ...
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ISBN:
(纸本)9781424458721;9781424458745
In this paper, a Improved evolutionary programming (IEP) is proposed to solve global numerical optimization problems with continuous variables. In the methodology, the well-known evolutionary programming (EP) is used as a basic level search, which can give a good direction to the optimal global region. Then, a local search (LS) procedure is adopted as a fine tuning to determine the optimal solution. IEP methodology enhances the computational accuracy and accelerates convergence rate at the later period of the searching by adopting LS operator. The combination approach contributes to the local exploration and the global exploration of IEP. The proposed method is effectively applied to solve 12 benchmark problems. Results show a satisfactory improvement in comparison with the standard EP.
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