摘要： 遗传参数的自适应调整是一个复杂的不确定性过程。为此，利用云模型优良的不确定性知识表示能力，提出一种改进的自适应遗传算法。该算法以自然语言为切入点，用云模型表达先验规则知识，通过云控制器调整遗传参数。函数优化实验表明，该算法能够较好地模拟迭代中参数的自适应调整过程，算法性能是可行、有效的。

关键词: 云模型, 遗传算法, 自适应算法, 参数优化, 函数优化

Abstract: Adjusting genetic parameter adaptively is a complex and uncertain process. Based on the cloud model, which can represents the uncertain knowledge, an improved adaptive Genetic Algorithm(GA) is proposed, and its basic principle and implementation strategy are discussed, the proposed method takes nature language as the cut-in point, and depicts uncertain prior rules by cloud model, adjusts the parameters of GA through cloud controll. Experiments of function optimization show that the proposed algorithm simulates the uncertain procedure of parameter adjusting, and its results are feasible and effective.

Key words: cloud model, Genetic Algorithm(GA), adaptive algorithm, parameter optimization, function optimization

中图分类号: 

• TP18