Multiobjective evolutionary algorithms for context-based search

Formulating high-quality queries is a key aspect of context-based search. However, determining the effectiveness of a query is challenging because multiple objectives, such as high precision and high recall, are usually involved. In this work we study techniques that can be applied to evolve contextualized queries when the criteria for determining query quality are based on multiple objectives. We report on the results of three different strategies for evolving queries: (1) single-objective, (2) multi-objective with Pareto-based ranking, and (3) multi-objective with aggregative ranking. After a comprehensive evaluation with a large set of topics we discuss the limitations of the singleobjective approach and observe that both the Pareto-based and aggregative strategies are highly effective for evolving topical queries. In particular, our experiments lead us to conclude that the multi-objective techniques are superior to a baseline as well as to well-known and ad-hoc query reformulation techniques.