"基于Matlab的遗传算法研究及优化【精品毕业设计】"

The study and application of genetic algorithms is a crucial and widely used method in optimization problems. This paper explores the basic concepts, components, characteristics, models, applications, and future research directions of genetic algorithms. Using Matlab 7.0, the paper focuses on solving the function optimization problem using genetic algorithms. By following the steps of genetic algorithms – encoding, decoding, calculating fitness, selection, crossover, and mutation – the paper aims to find the optimal value of a function. Additionally, the paper discusses improvements to the genetic algorithm for function optimization by adjusting parameters such as crossover probability and mutation probability to further optimize the results. This research provides valuable insights into the efficiency and effectiveness of genetic algorithms in solving optimization problems, with potential applications in various fields. Keywords: Genetic algorithm, fitness, crossover probability, mutation probability.