Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.

查询权重优化是信息检索系统中的一个重要课题，旨在寻找查询词权重的最优组合对相关文档进行排序。由于巨大的搜索空间，查询优化问题是棘手的，进化算法已经成为一种流行的方法。但是随着数据库规模的增长，传统的检索方式可能会返回大量的结果，导致效率低下，实用性差。为了解决这个问题，本文提出了一种基于交互式多模态遗传算法（IMGA）的两阶段信息检索系统，用于查询权重优化系统。拟议的 IMGA 有两个阶段：数量控制和质量优化。在数量控制阶段，多模态遗传算法借助 Niching 方法同时选择多个有希望的查询词组合，从而将检索到的文档数量控制在适当的范围内。在质量优化阶段，设计了交互式遗传算法来寻找最佳查询权重，从而产生对用户最友好的文档检索序列。用户的反馈信息将加速优化过程，遗传算法（GA）与相关反馈机制的作用交互执行。代替用户评价，建立数学模型来评价个体的适应度值。在提出的两阶段方法中，不仅可以控制返回结果的数量，而且可以提高检索的质量和准确性。所提出的方法是在拥有 2000 多个文档的数据库上运行的。实验结果表明，我们提出的方法在准确率和召回率方面优于几种最先进的查询权重优化方法。