Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
NIL-UCM: most-frequent-value-first attribute selection and best-scoring-choice realization
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
UCM submission to the surface realization challenge
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Information Processing and Management: an International Journal
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We propose the use of evolutionary algorithms (EAs) (Holland, 1992) to deal with the attribute selection task of referring expression generation. Evolutionary algorithms operate over a population of individuals (possible solutions for a problem) that evolve according to selection rules and genetic operators. The fitness function is a metric that evaluates each of the possible solutions, ensuring that the average adaptation of the population increases each generation. Repeating this process hundreds or thousands of times leads to very good solutions for the problem.