基于改进遗传算法的数据特征分类
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摘要
针对传统遗传算法在数据特征分类过程中容易陷入局部最佳解,分类结果识别率以及准确率较低的问题,提出基于改进遗传算法的数据特征分类方法。采用模拟退火法对遗传算法实施改进,遗传算法经过设置参数、适应度函数的设计、选择策略、交叉策略以及终止条件等过程得到粗糙数据特征分类结果。采用模拟退火算法通过概率突跳特性在温度下降时随机获取目标函数的全局最优解,基于Meteopolis准则提高算法局部寻优效率,通过模拟退火算法对遗传算法的交叉概率与变异概率的选择过程实施改进,获取高精度的数据特征分类结果。实验结果表明,所提方法数据特征分类识别率以及准确率高,分类耗时低。
As the traditional genetic algorithms may easily fall into the local optimal solution,and has low recognition rate and accuracy rate of classification results during the process of data feature classification,a method of data feature classification based on improved genetic algorithm is proposed. The simulated annealing method is adopted to improve the genetic algorithm which experiences the processes such as parameter setting,fitness function design,selection strategy,crossover strategy,and termination condition,so as to obtain the rough classification result of data features. The simulated annealing algorithm is adopted to randomly obtain the global optimal solution of the objective function by using the probability abrupt-jump feature when the temperature falls,the local optimizing efficiency of the algorithm is improved based on the Meteopolis criterion,and the selection process for crossover probability and mutation probability of the genetic algorithm is improved by means of the simulated annealing algorithm,so as to obtain high-accurate classification result of data features. The experimental results show that the proposed method has high recognition rate and accuracy rate of data feature classification and low classification time consumption.
As the traditional genetic algorithms may easily fall into the local optimal solution,and has low recognition rate and accuracy rate of classification results during the process of data feature classification,a method of data feature classification based on improved genetic algorithm is proposed. The simulated annealing method is adopted to improve the genetic algorithm which experiences the processes such as parameter setting,fitness function design,selection strategy,crossover strategy,and termination condition,so as to obtain the rough classification result of data features. The simulated annealing algorithm is adopted to randomly obtain the global optimal solution of the objective function by using the probability abrupt-jump feature when the temperature falls,the local optimizing efficiency of the algorithm is improved based on the Meteopolis criterion,and the selection process for crossover probability and mutation probability of the genetic algorithm is improved by means of the simulated annealing algorithm,so as to obtain high-accurate classification result of data features. The experimental results show that the proposed method has high recognition rate and accuracy rate of data feature classification and low classification time consumption.
引文
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[3]DEVI B R,RAO K N,SETTY S P.Towards better classification using improved genetic algorithm and decision tree for dengue datasets[J].International journal of applied engineering research,2015,10(8):20313-20326.
[4]顾键萍,张明敏,王梅亮.基于改进遗传算法的路径选择算法及仿真实现[J].系统仿真学报,2016,28(8):1805-1811.GU Jianping,ZHANG Mingmin,WANG Meiliang.Improved genetic algorithm-based network game path selection and simulation[J].Journal of system simulation,2016,28(8):1805-1811.
[5]陈国彬,张广泉.基于改进遗传算法的快速自动组卷算法研究[J].计算机应用研究,2015,32(10):2996-2998.CHEN Guobin,ZHANG Guangquan.New algorithm for intelligent test paper composition based on improved genetic algorithm[J].Application research of computers,2015,32(10):2996-2998.
[6]蒙秋男,娄剑,白雪.基于改进遗传算法的实际成本结转方法[J].管理评论,2016,28(1):179-190.MENG Qiunan,LOU Jian,BAI Xue.Carryover for actual cost based on improved genetic algorithm[J].Management review,2016,28(1):179-190.
[7]杨景明,顾佳琪,闫晓莹,等.基于改进遗传算法优化BP网络的轧制力预测研究[J].矿冶工程,2015,35(1):111-115.YANG Jingming,GU Jiaqi,YAN Xiaoying,et al.Rolling force prediction based on BP network optimized by an improved genetic algorithm[J].Mining and metallurgical engineering,2015,35(1):111-115.
[8]柴良勇,殷礼胜,甘敏,等.基于改进遗传算法的交通流量小波网络预测[J].合肥工业大学学报(自然科学版),2016,39(7):900-905.CHAI Liangyong,YIN Lisheng,GAN Min,et al.Prediction of traffic flow with wavelet network based on improved genetic algorithm[J].Journal of Hefei University of Technology(Natural science),2016,39(7):900-905.
[9]丁汉卿,王旭阳,葛彤.基于改进遗传算法的ROV推进器伺服系统辨识[J].哈尔滨工程大学学报,2017,38(2):168-174.DING Hanqing,WANG Xuyang,GE Tong.Parameter identification of variable displacement of an remote operated vehicle hydraulic thruster based on an improved genetic algorithm[J].Journal of Harbin Engineering University,2017,38(2):168-174.
[10]JIANG K,LU J,XIA K.A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE[J].Arabian journal for science&engineering,2016,41(8):3255-3266.