Plain text corpora contain much information which can only be accessed through human annotation and semantic analysis, which is typically very time consuming to perform. Analysis of such texts at a syntactic or grammatical structure level can however extract some of this information in an automated manner, even if identifying effective rules can be extremely difficult. One such type of implicit information present in texts is that of definitional phrases and sentences. In this paper, we investigate the use of evolutionary algorithms to learn classifiers to discriminate between definitional and non-definitional sentences in non-technical texts, and show how effective grammar-based definition discriminators can be automatically learnt with minor human intervention.