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As an effective global search method, genetic algorithm has obvious advantages. But it usually has problems of premature convergence and local optimum in practical application. According to this, a new algorithm with improved selection, crossover and mutation is proposed. Through the simulation experiments, the improved algorithm shows its faster convergence and better stability. It is valid which...
In view of shortcomings such as premature convergence and oscillation in Simple Genetic Algorithms (SGA), the article adopts the method of adaptive adjusting for the probabilities of crossover and mutation. So the Improved Genetic Algorithms (IGA) is formed by adding the transgenic operator to SGA. And the optimum design program of steel box-concrete composite arch bridge based on improved genetic...
Premature convergence is the main obstacle to the application of genetic algorithm. This paper makes improvement on traditional genetic algorithm by linear scale transformation of fitness function, using self-adaptive crossover and mutation probability and adopting close relative breeding avoidance method. Simulation results show that the improved algorithm outperforms traditional genetic algorithm...
Aiming at shortening the design time of the optimal test cases, a model of Collaborative Multi-Agent based on Parallel Genetic Algorithm (CMA-PGA) is put forward, which is to be used in combinatorial testing of the software system. Because of the competition and coordination mechanisms especially designed in CMA-PGA, the convergence rate of this model outperforms that of simple genetic algorithm in...
The large computing capacity provided by grid systems is beneficial for solving complex problems by using many nodes of the grid at the same time. The usefulness of a grid system largely depends, among other factors, on the efficiency of the system regarding the allocation of jobs to grid resources. This paper proposes an Roulette Wheel Selection Genetic Algorithm using Best Rank Power(PRRWSGA) for...
In this paper we study how the connectivity affects the performance of insular parallel genetic algorithms (PGAs). Seven topologies PGAs were proposed, with growing number of connections. We used three instances of the well-known traveling salesman problem as benchmark. Each island of the PGA had different parameters and we established a fixed migration policy for all islands. Experiments were done...
We propose a system of Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are central to reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed...
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration...
The present study proposes an alternate method based on genetic algorithm (GA) to estimate the subsurface lithologic parameters such as P-wave velocity, the S-wave velocity and the density for subsurface earth layers occurring at a particular location. These are useful parameters to discriminate lithology and help in detecting hydrocarbons from seismic data. However, estimation of the lithologic parameters...
In this paper the comparative optimal designs for maximum sidelobe level (SLL) reduction of three-ring concentric circular antenna array (CCAA) are determined using two novel Particle Swarm Optimization (PSO) techniques namely Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA), Craziness based Particle Swarm Optimization (CRPSO) and Binary coded Genetic Algorithm...
Color selection in designing user interfaces is addressed by an interactive genetic algorithm. The proposed approach is aimed at finding the optimal trade-off between different and sometimes conflicting constraints, without any explicit model of user preferences and abilities. Experimentation investigates the algorithm convergence under several conditions and user behavior.
This work presents system identification using neural network approaches for modelling a laboratory based twin rotor multi-input multi-output system (TRMS). Here we focus on a memetic algorithm based approach for training the multilayer perceptron neural network (NN) applied to nonlinear system identification. In the proposed system identification scheme, we have exploited three global search methods...
Genetic Algorithms have been a very effective tool in solving highly non-linear problems in various disciplines. They are capable of finding an acceptable solution, even if number of acceptable solutions happens to be very small percentage of the total number of possible solutions. However the convergence rate may become very slow when the solution space happens to be very large and rugged. In this...
Because there were a lot of facts that affect the intensity of coal and gas outburst, a BP neural network model for forecasting the intensity was constructed. Aimed at the shortcoming of the BP neural network, such as the slow training speed, easy to be trapped into the local optimums, and the premature convergence of genetic algorithm (GA) BP neural network, a method to design the BP neural network...
Proposing a new algorithm which is simple but effective. Using characteristic of biological evolution and common sense to design the selection operator, improve the variation method of the crossover probability and the mutation probability. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence speed, convergence precision,...
The commercial banks risks come from all the uncertainty of the banking business, which have diffusibility and hidden features, if not timely controlled, will have a negative impact on the national economy. Therefore, it is necessary to design the corresponding index system according to the objectivity and relativity of the banking risks, and then control quantitatively the banks risk. Based on the...
This paper introduces triangulation theory into genetic algorithm and with which, the optimization problem will be translated into a fixed point problem. An improved genetic algorithm is proposed by virtue of the concept of relative coordinates genetic coding, designs corresponding crossover and mutation operator. Through genetic algorithms to overcome the triangulation of the shortcomings of human...
This paper introduces triangulation theory into genetic algorithm and with which, the optimization problem will be translated into a fixed point problem. An improved genetic algorithm is proposed by virtue of the concept of relative coordinates genetic coding, designs corresponding crossover and mutation operator. Through genetic algorithms to overcome the triangulation of the shortcomings of human...
A novel adaptive genetic algorithm (NAGA), which improves the global search ability and convergence of solutions by adjusting the crossover and mutation probability automatically, is presented for the design optimization of linear induction motors (LIM). Results by the proposed algorithm are compared with another algorithm to demonstrate the superiority and feasibility of the proposed NAGA.
Based on the analysis of the premature convergence causing in genetic algorithm, and enlightened by the biology migratory phenomenon and Inversion Operator, a cyclic shift genetic algorithm (CSGA) was presented and discussed with different strategy. CSGA can effectively suppress the premature phenomenon of standard genetic algorithm (SGA), decrease the dependence of SGA to the character of initial...
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