Context-sensitive text mining with fitness leveling Genetic Algorithm

Abstract

Contextual processing is a great challenge for information retrieval study - the most approved techniques include scanning content of HTML web pages, user supported metadata analysis, automatic inference grounded on knowledge base, or content-oriented digital documents analysis. We propose a meta-heuristic by making use of Genetic Algorithms for Contextual Search (GACS) built on genetic programming (GP) and custom fitness leveling function to optimize contextual queries in exact search that represents unstructured phrases generated by the user. Our findings show that the queries built with GACS can significantly optimize the retrieval process.