Using genetic algorithms to create meaningful poetic text

Ruli Manurung, Graeme Ritchie, Henry Thompson

Research output: Contribution to journalReview articlepeer-review

74 Citations (Scopus)


This article presents a series of experiments in automatically generating poetic texts. We confined our attention to the generation of texts which are syntactically well-formed, meet certain pre-specified patterns of metre and broadly convey some given meaning. Such aspects can be formally defined, thus avoiding the complications of imagery and interpretation that are central to assessing more free forms of verse. Our implemented system, McGONAGALL, applies the genetic algorithm to construct such texts. It uses a sophisticated linguistic formalism to represent its genomic information, from which can be computed the phenotypic information of both semantic representations and patterns of stress. The conducted experiments broadly indicated that relatively meaningful text could be produced if the constraints on metre were relaxed, and precise metric text was possible with loose semantic constraints, but it was difficult to produce text which was both semantically coherent and of high quality metrically.

Original languageEnglish
Pages (from-to)43-64
Number of pages22
JournalJournal of Experimental and Theoretical Artificial Intelligence
Issue number1
Publication statusPublished - 1 Mar 2012


  • creative language
  • genetic algorithms
  • natural language generation
  • poetry


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