Theory And Application Of Genetic Algorithm Based On Artificial Neural Network

Optimization theory, computer technology and engineering technology have combined closely, forming optimum technology, which is a modern design method, and technology. Facing the complex project design problem, application of optimum technology can complete the optimization of design project quickly , improve efficiency and quality of design. By development of optimum field and object, optimum technology and method is more highly demanded. With development of computer technology, speed of compute runs quick, ability of solution scale and wild use are enhanced, so Seeking the optimum whole solution becomes the main goal of optimum pursuit. Traditional optimum method has not satisfied requirements. Research of new optimum algorithm becomes one of hotspot.In the recent years, GA (Genetic Algorithm), SAA (Simulated Annealing Algorithm) and ANN (Artificial Neural Network), these modern comprehensive optimization method gain great success in machine study, process control, economy forecast, engineering optimization field, which interest scientist in maths, physics, chemistry, biology, computer science, social science, economics, engineering application field.In chapter 1 introduction analyzes the development of optimization theory and actuality of optimum design application carefully . From lots of materials, summarize these new algorithms used widely in many field. Algorithms are incomplete themselves and exist lack in some aspect of actual application. In chapter 2, Present a new kind of Genetic combined many point continual annealing Algorithm .The method starts to operate with Genetic Algorithm, follow with simulated annealing Algorithm .The key is how to select mutation factor, fundamental temperature, temperature adjustment. Section 3 in chapter 2 describes basic process and model with mathematical method, analyzes penalized factor, mutation temperature. For two examples, resulttestifies that it is feasible to use in nonlinear restriction optimization.Chapter 3 is important part in the paper. Analyze and research BP network, concealed node number of network former frame newly. Give new supplement and advice about How to select study velocity and activation function in BP algorithm. Construct a new method about net topological frame. Give a new determinant principle in ANN constringency.Chapter 5 exerts visual technology to train net and to simulate data. Analyze feasibility and superiority of genetic algorithm based on ANN. Write the kind of arithmetic program with OOP and optimizes weight of the crane arm frame system with software. Final result proves optimum result better.Theory united with practice is the feature of this paper. Chapter 7 sums up the research work and directs the following research afterwards.