"基于Matlab的遗传算法研究及改进-本科毕业设计论文"

In the undergraduate thesis "Research and Simulation of Genetic Algorithm based on Matlab", the author explores the basic principles, components, characteristics, models, applications, and future research directions of genetic algorithms. The research focuses on using genetic algorithms to optimize functions in Matlab 7.0, following the steps of encoding, decoding, calculating fitness (function value), selection and replication operation, crossover operation, and mutation operation. In the third part of the thesis, the author discusses the improvements made to the genetic algorithm for optimizing function values. This section mainly focuses on changing the basic operating parameters of the genetic algorithm, such as adjusting the crossover probability (Pc) and mutation probability (Pm) values to make the optimal value closer to the true maximum of the function under the standardized conditions. Overall, the study delves into the practical application of genetic algorithms in optimizing functions, showcasing the potential for improving efficiency and accuracy in solving optimization problems through algorithmic approaches. The research contributes valuable insights to the field of genetic algorithms and highlights the importance of parameter adjustments in achieving optimal solutions.