Auxiliary Population Multitask Optimization Based on Chinese Semantic Understanding

In Chinese language semantic analysis, the processed languages often reveal similar representations in models for different application scenarios, resulting in similar language models. With that characteristic, evolutionary multitask optimization (EMTO) algorithms, which realize the synergy optimization for multiple tasks, have the potential to optimize such models for different scenarios. EMTO is an emerging topic in evolutionary computation (EC) for solving multitask optimization problems (MTOPs) with the help of knowledge transfer (KT). However, the current EMTO algorithms often involve two limitations. First, many KT methods usually ignore the distribution information of populations to evaluate task similarity. Second, many EMTO algorithms often directly transfer individuals from the source task to target task, which cannot guarantee the quality of the transferred knowledge. To overcome these challenges, an auxiliary–population–based multitask optimization (APMTO) is proposed in this paper, which will be further applied to Chinese semantic understanding in our future works. We first propose an adaptive similarity estimation (ASE) strategy to exploit the distribution information among tasks and evaluate the similarity of tasks, so as to adaptively adjust the KT frequency. Then, an auxiliary-population-based KT (APKT) strategy is designed, which uses auxiliary population to map the global best solution of the source task to target task, offering more useful transferred information for the target task. APMTO is tested on multitask test suite CEC2022 and compared with several state–of–the–art EMTO algorithms. The results show that APMTO outperforms the compared state–of–the–art algorithms, which fully reveals its effectiveness and superiority.