摘要
将静态繁殖理论和机器学习原理引入到免疫遗传算法中,利用自适应疫苗,增强个体免疫力,以增加种群的平均适值,从而有效地避免了最优解的丢失,缩小了搜索空间,加快了进化速度,使系统能够在很短的时间内得到最优解。同时,针对典型车间调度问题,分别对改进算法和其他优化算法的计算结果进行了比较,表明改进算法更有效。
Theories of static multiplication and machine learning were introduced to immune genetic algorithm. The chromosomes' immunity was boosted and the average fitness of chromosomes was improved by using adaptive vaccine, as a result the loss of optimum solution was avoided, searching space was reduced and evolution speed was increased, then the optimal solution could be achieved earlier. At the same time, the calculation result of the mentioned algorithm was compared with other optimal algorithms in solving classic Job-shop Scheduling Problem.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2005年第7期1047-1050,共4页
Computer Integrated Manufacturing Systems
基金
辽宁省基金资助项目(20022114)。~~
关键词
静态繁殖
机器学习
免疫遗传算法
自适应疫苗
<Keyword>static multiplication
machine learning
immune genetic algorithm
adaptive vaccine
