Optimized weights of document keywords for auto-reply accuracy - Tsai, Jinn-Tsong (Neurocomputing (Amsterdam), 2014)
A Taguchi-crossover differential evolution (TCDE) algorithm is proposed to optimize weights of document keywords for auto-reply accuracy. The proposed TCDE algorithm combines the use of differential evolution for exploring the optimal feasible region in macro-space with the use of the Taguchi method for exploiting the optimal solution in micro-space. For learning purpose, an answer needs to be exactly given for a specific query. Notably, teachers give a problem answer to elementary students who need to have the clear and accurate solution for learning according to their queries. This study shows the TCDE which integrates a cosine similarity measure and an evaluation function to successfully find the best weights of document keywords for auto-reply accuracy. Performance comparisons confirm that the TCDE algorithm outperforms existing methods presented in the literature in finding the best weights of document keywords and obtaining accurate answers.