CAJ | 학술논문

提出并实现了利用遗传算法求解试题库组卷的数学模型,定义了组卷问题的适应度函数,讨论了运用遗传算法求解在一定约束条件下的多目标参数优化问题,通过初始化种群、选择算子、交叉算子和变异算子,等过程不断进化,最后得到最优解,实验结果表明,遗传算法相对于其它算法更能有效的解决试题库自动组卷问题,提出了实现不相邻试卷分配的补遗随机算法,为求解类似的多目标约束问题及不相邻组合问题提供一种新的方法.
제출병실현료이용유전산법구해시제고조권적수학모형,정의료조권문제적괄응도함수,토론료운용유전산법구해재일정약속조건하적다목표삼수우화문제,통과초시화충군、선택산자、교차산자화변이산자,등과정불단진화,최후득도최우해,실험결과표명,유전산법상대우기타산법경능유효적해결시제고자동조권문제,제출료실현불상린시권분배적보유수궤산법,위구해유사적다목표약속문제급불상린조합문제제공일충신적방법.
The paper introduces a mathematical model of testpaper assembling Oil genetic algorithm,defines an adaptive function on testpaper assembling,and provides some ideas on multi-object parameter optimization on restricted terms by genetic algorithm.In the evolutionary processes of seeds initialization,operators selecting,operator crossing,operation differentiation,the best solution is finally worked out.Results of experiments indicate,genetic algorithm is more efficient than other algorithms on testpaper auto-assembl-ing.Random algorithm which could achieves testpaper non-adjacency distribution,is a new method for similar multi-object restriction and non-adjacency combination problems.