Journal of the American Society for Information Science
Applying genetic algorithms to query optimization in document retrieval
Information Processing and Management: an International Journal
A test of genetic algorithms in relevance feedback
Information Processing and Management: an International Journal
Learning retrieval expert combinations with genetic algorithms
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
On using genetic algorithms for multimodal relevance optimization in information retrieval
Journal of the American Society for Information Science and Technology
Quantitative evaluation of passage retrieval algorithms for question answering
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Genetic algorithms in relevance feedback: a second test and new contributions
Information Processing and Management: an International Journal
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
Choosing document structure weights
Information Processing and Management: an International Journal
Learning to rank for why-question answering
Information Retrieval
Evolutionary optimization for ranking how-to questions based on user-generated contents
Expert Systems with Applications: An International Journal
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In this paper we compare four selection strategies in evolutionary optimization of information retrieval (IR) in a question answering setting. The IR index has been augmented by linguistic features to improve the retrieval performance of potential answer passages using queries generated from natural language questions. We use a genetic algorithm to optimize the selection of features and their weights when querying the IR database. With our experiments, we can show that the genetic algorithm applied is robust to strategy changes used for selecting individuals. All experiments yield query settings with improved retrieval performance when applied to unseen data. However, we can observe significant runtime differences when applying the various selection approaches which should be considered when choosing one of these approaches.