摘要
针对传统遗传算法的交叉率和变异率最优值难以确定及进化种群单一性等问题,提出一种双种群自适应遗传算法(DPAGA)。算法引入主导种群和辅助种群,并将生物界有性繁殖特征应用于交叉操作,让主导种群和辅助种群在进化过程中执行不同的交叉和变异策略,极大地提高了算法的寻优效率。在实践教学质量评价工作中,知识规则是实现教学质量评价的依据,为获得更加优质的知识规则,将DPAGA算法应用于实践教学质量评价知识规则挖掘。实验结果表明,基于改进遗传算法的知识规则挖掘方法是有效的,能够快速挖掘出优秀的新知识规则,使实践教学质量评价知识规则库能得以更新和发展。
Aiming at the problem of evolutionary population singleness and it is difficult to determine the optimal value of crossover rate and mutation rate of traditional genetic algorithm,a dual population adaptive genetic algorithm(DPAGA)is proposed.Dominant population and auxiliary population are introduced in the algorithm.Different crossover and mutation strategies are executed by dominant population and auxiliary population in the evolution process,and the optimization efficiency of the algorithm is greatly improved.In the practice teaching quality evaluation,knowledge rules are the basis to realize the teaching quality evaluation.In order to obtain better knowledge rules,DPAGA is applied in practice teaching quality evaluation knowledge rule mining.The experimental results show that the knowledge rule mining method based on improved genetic algorithm is effective.It can quickly mine excellent new knowledge rules.So the knowledge rule base of practice teaching quality evaluation can be updated and developed.
作者
严太山
王欣
Yan Taishan;Wang Xin(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414000)
出处
《现代计算机》
2022年第23期38-43,共6页
Modern Computer
基金
湖南省自然科学基金项目(2018JJ2151,2017JJ2107)
湖南省普通高等学校教学改革研究项目(HNJG-2021-0778)
教育部产学合作协同育人项目(202102211055)
湖南理工学院教学改革研究项目(2021A26)。
关键词
双种群自适应遗传算法
主导种群
辅助种群
知识规则挖掘
实践教学质量评价
dual population adaptive genetic algorithm
dominant population
auxiliary population
knowledge rule mining
practice teaching quality evaluation
