基于混合遗传算法的任务驱动分组优化研究 - 计算机科学.pdf

第 44 卷第6A 期 Vo l. 44 No. 6A 计算机科学 2017 年 6 月 CO岛1PUTER SCIENCE June 2017 基于混合遗传算法的任务驱动分组优化研究 李浩君杜兆宏邱飞岳 (浙江工业大学教育科学与技术学院 杭州 310023) 摘 要 智能算法应用到教学领域来实现自动分组具有重要意义。针对网络学习环境下任务驱动教学中如何按最优 分组方案进行小组划分的问题，综合考虑了分纽问题中学习者之间的特征差异和任务难易程度等影响因素，构建了基 于任务驱动分组优化问题的数学模型，提出了基于混合遗传算法的任务驱动分组优化策略。在 MATLAB7.0 平台 上，运用混合遗传算法对任务驱动的分组优化进行了仿真实验。实验结果表明，基于混合遗传算法的任务驱动分纽优 化是可行且有效的。 关键词 协作学习分组，任务驱动，分组优化模型，混合遗传算法 中图法分类号 TP39 1. 41 文献标识码 A Optimized Research for Task-driven Grouping ßased on Hybrid Genetic Algorithm LI Haσjun DU Zhao-hong QIU Fei 沪ue (College of Education Science and Technology. Zhejiang University of Technology , Hangzhou 310023 ,China) Abstract Intelligent algorithm that applies to the education field to realize automatic grouping has great significance. In the task-driven teaching under the network learning environment for how to group divided according to the optimal grouping scheme ,the factors of the characteristic differences between learners and the degree of task difficulty were considered ,a mathematical model based on task-driven grouping optimization problem was built ,the strategy of task- driven grouping optimization based on hybrid genetic algorithm was proposed. We had done an simulation experiment by using hybrid genetic algorithm on MATLAB7. 0 platfo rm. Experimental results show that the optimization of task-dri- ving grouping based on hybrid genetic algorithm is feasible and effective. Ke归10时s Collaborative learning group ,Task-driv凹，Grouping optimization model , Hybrid genetic algorithm Moreno 等提出了一种利用改进遗传算法来解决分组问题的 引言