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Combining with genetic algorithm, the improved estimation of distribution algorithm (EDA) is provided. The crossover and mutation operations are added and the "elite" individuals are retained, which can keep the excellent evolution mode. The selection based on energy entropy is added, which can explore the solution space sufficiently and keep the population diversity. A neural network with...
In traditional simulation calculation of concrete filled steel tubular (CFST) arch bridge, to find out the initial state of the backward analysis is very difficult due to the force-bearing characteristics of CFST arch bridge. Genetic algorithm (GA), as a general-purposed global optimization algorithm, has the disadvantages of the premature phenomenon and poor performance in local optimization. In...
In order to avoid the premature convergence and improve convergence rate, a novel adaptive genetic algorithm for reactive power optimization is discussed in detail. In reproduction operator, the method of retaining optimal individual is used to ensure the convergence and at the same time, the competition method is also adopted to keep the better dispersal of all individuals. In Mutation operator,...
Based on the optimization problem of the number and size in coal mine equipment the principle and procedure of genetic algorithm is introduced. The case of application proves that the genetic algorithm can better optimize the number and size of equipments in coal mine.
Destruction of interdependencies of multivariable in decomposing hyper-high dimensional problems into single variable is generally the main reason that Conventional CC framework fails to optimize inseparable problems. An improved CC framework is proposed, which designs a basic optimizer that has better performance in high-dimension optimization. The optimizer is a simplex-based genetic algorithm (HD-simplex...
The constraint conditions of the auto-generating test paper are analyzed. The mathematical model of intelligence test paper generation system is set up and a new method of composing test paper based on the improved genetic algorithm is given. The result of the experiments shows that the new method is more efficient and easier to deal with the problem of autogenerating test paper than the traditional...
A modified dynamic differential evolution was proposed for discrete optimization. Based on the new framework of dynamic differential evolution, two additional operators were used to extend the dynamic differential evolution to the field of discrete optimization. The first operator was the mapping operator, which could map the continuous value into zero or one. The other new operator was the boundary...
Genetic algorithm, one of the new methods for global non-linear optimization problem, has been applied in magnetotelluric data analysis. In this paper, the magnetotelluric inverse problem was studied by a hybrid genetic algorithm, which was based on the combination of simplex method and genetic algorithm. The standard genetic algorithm has poor local search ability, large amounts of calculation, and...
Considering the problem of the local optimization in the adaptive genetic algorithm (AGA), this paper presents an improved adaptive genetic algorithm (IAGA) which can optimize the weights and thresholds of the neural network. A stock prediction system based on neural networks and fuzzy theory is designed. According to the analysis of the history data of the stock, the system predicts this stock's...
In order to find efficient and robust structural optimum design method for the deformable mirror (DM) support structure, this paper analyzes a kind of circle structure based on the finite element analysis results of ANSYS software. Aiming at finding the maximum resonate frequency of this support structure, an intelligent solution combining orthogonal experiment, BP neural network and genetic algorithms...
Simple genetic algorithm is prone to premature and has a slow convergence. In view of the above inadequacies, we carried on an optimal design of selection operation and proposed a model of selection operation creatively in this paper. That is the strategy of ultra expectations - compared selection. Besides, we proved its advantages in global optimal solution and convergence speed. It can avoid effectively...
In this paper, the theories of genetic algorithm (GA) and back propagation (BP) algorithm are introduced. For the purpose of overcoming the disadvantages of standard BP algorithm, such as local optimum and low convergence speed, the paper adopts genetic algorithm optimizing BP neural network for training. By analyzing computer stimulation results and comparing with traditional blind equalization algorithm,...
Inspired from the experience of genetic and clone algorithm, propose an algorithm of danger model immune algorithm (DMIA) according to the danger model theory. It has a good performance in function optimization. From the simulation result, we can see that DMIA is valid, and also with higher efficiency than genetic algorithm.
According to the functional demand of intelligent Test Paper system, we have designed four functional modules: examination database, test paper-generation, grade analysis and system setup. Test paper generation module is the core of the system, The improved Genetic Algorithms is more efficient and easier to get over premature convergence than the traditional algorithms. It is proved by a number of...
Neural network and genetic algorithm have attracted a great deal of attention as methods and theories realizing artificial intelligence recently. The combination of these two is drawing more and more attention. This paper demonstrates the possibility of combining neural network with genetic algorithm. An improved genetic algorithm for the learning of neural network's connection weights is presented...
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