Genetic algorithms for topical web search: A study of different mutation rates

Abstract

Harvesting topical content is a process that can be done by formulating topic-relevant queries and submitting them to a search engine. The quality of the material collected through this process is highly dependant on the vocabulary used to generate the search queries. In this scenario, selecting good query terms can be seen as an optimization problem where the objective function to be optimized is based on the effectiveness of a query to retrieve relevant material. Three characteristics of this optimization problem are (1) the high-dimensionality of the search space, where candidate solutions are queries and each term corresponds to a different dimension, (2) the existence of acceptable suboptimal solutions, and (3) the possibility of finding multiple solutions. This paper describes optimization techniques based on Genetic Algorithms to evolve “good query terms” in the context of a given topic. We discuss the use of a mutation pool to allow the generation of queries with novel terms, and study the effect of different mutation rates on the exploration of query-space.