中北大学学报（自然科学版）2025，Vol.46Issue(2)：208-218,11.DOI:10.62756/jnuc.issn.1673-3193.2023.11.0009
基于Optuna优化的遗传算法智能组卷模型
Intelligent Exam Paper Generation Model Based on Optuna Optimized Genetic Algorithm
摘要
Abstract
In order to improve the performance of intelligent exam paper generation,and solve the prob-lem that the parameters of the genetic algorithm based model are difficult to determine in practice,and the performance is unstable in the face of question banks with different sizes and feature distributions,an intel-ligent exam paper generation model based on Optuna optimized genetic algorithm was proposed.By designing hierarchical gray coding,the Hamming cliff problem caused by traditional binary and decimal encoding methods was overcome.The population size,number of iterations,and other parameters of the genetic algorithm were determined by Optuna optimization self-feedback model,and the crossover and mutation rate of the genetic algorithm were dynamically adjusted to achieve adaptive adjustment of the exam paper generation search space.Experimental results show that the proposed algorithm can effectively determine the parameters,and realize dynamic adjustment.Final exam paper generation quality is better than other models based on random and heuristic algorithms.关键词
智能组卷/遗传算法/OptunaKey words
intelligent exam paper generation/genetic algorithm/Optuna分类
计算机与自动化引用本文复制引用
常宸,胡安波,高鹏..基于Optuna优化的遗传算法智能组卷模型[J].中北大学学报（自然科学版）,2025,46(2):208-218,11.基金项目
国家社会科学基金重点项目(2022-SKJJ-B-072) （2022-SKJJ-B-072）
国防科技战略先导计划(21-ZLXD-02-00-02-006-38) （21-ZLXD-02-00-02-006-38）
