摘要
Memetic算法是一种启发式搜索方法,常用于解决一些NP问题。本文通过对遗传Memetic算法的改进与优化,结合智能组卷问题的特点,提出一套完整的解决方案。算法使用Memetic算法框架,全局搜索策略采用分段实数编码的遗传算法,融合了算法的交叉变异操作,局部搜索策略采用模拟退火算法,有效解决陷入局部最优问题。通过不同算法的对比实验表明,本文提出的Memetic算法能够快速高效地解决智能组卷问题,大大提升试卷生成质量,减少迭代次数,可快速获得最优解。
Memetic algorithm is a metaheuristic search method. It is often used to solve NP problems. In this paper, through the improvement and optimization of the genetic Memetic algorithm, combined with the characteristics of the intelligent test paper gen- eration, a set of complete solution is put forward. The algorithm uses the Memetic algorithm framework; the global search strategy uses genetic algorithm of piecewise real number encoding; crossover and mutation operations are included in. The local search strategy algorithm using simulated annealing algorithm, solves the local optimization problem effectively. Through the comparison experiment of different algorithms, the experimental results show that the Memetic algorithm proposed in this paper can solve the problem of generating test paper quickly and efficiently, at the same time, the algorithm can improve the quality of test paper, and also can reduce the number of iterations and obtain the optimal solution more quickly.
出处
《计算机与现代化》
2016年第11期114-117,121,共5页
Computer and Modernization
关键词
智能组卷
MEMETIC算法
遗传算法
模拟退火算法
generating test paper intelligently
Memetic algorithm
genetic algorithm
simulated annealing algorithm
