关键词: 遗传算法, 神经网络, 模拟退火, 分类器

Abstract: An Improved Genetic Algorithms(IGA) was presented. IGA adopted crossover probability and mutation probability decided by individual’s fitness, introduced simulated annealing methods after crossover, and improved operators of Simple Genetic Algorithms(SGA), in order to avoid drawbacks such as prematurity and bad local search ability etc of SGA. In this paper, classifiers of neural networks were constructed using IGA and SGA. Experiment results show that IGA performs better than SGA on the best fitness and the best classifying veracity.

Key words: genetic algorithms, neural networks, simulated annealing, classifier