Abstract: To deal with the prematurity and low convergence speed when the genetic algorithm is used for global optimization, a rough set attribute reduction algorithm based on adaptive GA was proposed. Based on the adaptive crossover operator and mutation operator that adjust the crossover probability and mutation probability of each individual, the selection probability of every individual of the population was optimized in this algorithm. Experimental results show that the algorithm can evidently improve global optimization capability and convergence speed.

Key words: Rough set, Adaptive genetic algorithm, Attribute reduction

摘要： 针对遗传算法在全局优化问题中出现的早熟收敛和后期收敛速度较慢的现象，提出了一种基于自适应遗传算法的粗糙集属性约简方法。该算法基于自适应交叉概率算子和变异算子，根据进化代数和群体的适应值，动态调整各个个体的交叉概率和变异概率，优化了各个个体被选择的概率。实验表明,该方法能够明显地改善全局寻优能力，并大大加快了收敛速度。

关键词: 粗糙集, 自适应遗传算法, 属性约简 