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Using genetic algorithm and BP neural network method of combining, this paper has established dynamic forward feedback correction model and has completed the automatic adjustment of the various parameters required for rolling steel pipe, and has made rolled steel pipe system work at the best value. After the actual data validation, the model can more accurately pre-adjusted parameters to achieve intelligent...
Computer games that handle realistic environments are becoming more popular in the game market. Games that make use of natural environments such as the spreading of fire or the flow of water need to be very carefully designed. In order to produce a desired effect of fire or water, a designer needs to try and test map properties several times. There has been an effort to use genetic algorithm to find...
In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network's hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized...
Although simple genetic algorithm (SGA) can, to some extent, improve the back propagation neural network (BP), it is prone to prematurity and losing the optimal solutions. Niche technology and fuzzy control theory are introduced to improve SGA and the improved one is used to optimize BP. The improved genetic algorithm is used to optimize BP neural network. In addition, due to the increasingly voltage...
This paper proposes an approach to find solution to the Bounded Knapsack Problem (BKP). BKP is a generalization of 0/1 knapsack problem in which multiple instances of distinct items but a single knapsack is considered. This problem occurs in many ways in real-life, such as cryptography, finance, etc. A genetic algorithm using greedy approach is proposed to solve this problem. The experiments prove...
Traffic flow prediction plays an important role in urban traffic management and control. Traditional prediction methods are mostly difficult to meet the high complexity, randomness and uncertainty characteristics of urban traffic flow. In this paper, a new prediction model is proposed based on self-adaptive neural network. Compared with other methods, it possesses the advantages of low computational...
The load balancing scheduling is the core of the load balancing technology in the cluster system. The actual load of servers will increase suddenly before the load value is updated if many clients link the servers in a short period. A mathematical model of load balancing was improved and an adaptive load balancing optimization scheduling based on genetic algorithm was proposed, analyzed and simulated...
The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on Hilbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in this paper. Then the RBF_NN is used to predict the signal before EMD. The...
One of the most important issues for autonomous mobile robots is finding paths in their environment. A local path planner must be able to design the path immediately and if possible with high accuracy and efficiency. In this paper genetic algorithm is used in order to devise a path planner that reaches high accuracy like global path planners and at the same time with acceptable speed like local path...
Air Combat Decision-Making for Coordinated Multiple Target Assignment is an important yet difficult problem in the modern information warfare. Previous methods, such as neural network, genetic algorithm, ant colony algorithm, particle swarm optimization and auction algorithm, used to resolve this problem have proved to be either too brittle or not stable. To address this problem, a new continuous...
A messy genetic algorithm (mGA), speculated by David Goldberg in 1989, is regarded as one of the most efficient method on those problems with Building Blocks (BB). While Hierarchical-if-and-only-if (HIFF) test problem is the basic example of a hierarchically consistent building-block problem. As it features hierarchical BB structure with multiple optima, HIFF test problem becomes the hardest test...
Cutting stock is concerned with how to saving material, optimize resources in product designing, manufacturing. It is very complicated and difficult in computing theory, but it has got the extensive application in the actual manufacture. In allusion to the actual characteristics of the genetic algorithm which is applied in the problem of the optimization in rectangular pieces of material, this paper...
In the paper a novel improved genetic algorithm is proposed based on the maximum entropy for thresholding image segmentation. First of all, the encoded mode is made and the maximum entropy function is selected as the key adaptation genetic algorithm, and then the initial group is generated by roulette selection algorithm to the next generation for the best individual, which can improve the global...
Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing...
Time series forecasting is the main method in network flow prediction. RBF neural network is capable of universal approximation, which not only has fast training velocity, but also can solve the local minima problem. Thus, network flow prediction technology based on genetic algorithm and RBF neural network is presented in the paper. And the training parameters are adjusted by genetic algorithm. Network...
An improved and colony algorithm was proposed. Genetic algorithm was utilized to optimize the parameters of ant colony algorithm. The improved algorithm was used to solve the optimization routing of the basic vehicle routing problem. The algorithm possesses some characteristics such as strong total researching ability. The experimental results show that the improved ant colony algorithm possesses...
The multi-echelon inventory control of weapon equipment repairable spare parts is an important problem for equipment support. In this paper, a genetic algorithm for the multi-inventory problem of repairable spare parts was proposed. In the genetic algorithm, a suitable chromosome representation for multi-inventory problem was presented, and the crossover and mutation operators were investigated. To...
High temperature during system-on-chip (SoC) test often suffers from critical problems such as timing errors, decrease in reliability and even potential damage to chip under test. Thermal-aware test scheduling is an efficient method for ensuring thermal safe during test. The temperature evaluation is a significant research work during thermal-aware test scheduling. A simple and effective temperature...
In this paper with studying of all parameters in grid environment a new scheduling algorithm for independent task is introduced according to Genetic Algorithm. This algorithm can be more efficient and more dependable than similar previous algorithms. The simulated results and reasons for reaching to better makespan and more efficiency in the grid environment. In the grids with high fault with high...
A huge number of routing protocols have been proposed for ad hoc networks to improve the networks' performance. Because of the large number of designs, it's difficult to track all the designs and choose the optimal protocol for an ad hoc network under various scenarios. In addition, it is not clear if any of the original design ideas in the huge number of existing works can be combined to form new...
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