Divide-and-conquer memetic algorithm for online multi-objective test paper generation

Online test paper generation (Online-TPG) generates a test paper automatically online according to user specification based on multiple assessment criteria, and the generated test paper can then be attempted online by user. Online-TPG is challenging as it is a multi-objective optimization problem that is NP-hard, and it is also required to satisfy the online generation requirement. In this paper, we propose an efficient multi-objective optimization approach based on the divide-and-conquer memetic algorithm (DAC-MA) for Online-TPG. Instead of solving the multi-objective constraints simultaneously, the set of constraints is divided into two subsets of relevant constraints, which can then be solved separately and effectively by evolutionary computation and local search of DAC-MA. The empirical performance results have shown that the proposed approach has outperformed other TPG techniques in terms of runtime efficiency and paper quality.