This work presents some applications of Evolutionary Programming to
different tasks of Natural Language Processing (NLP). First of all, the
work defines a general scheme of application of evolutionary techniques
to NLP, which gives the mainstream for the design of the elements of
the algorithm. This scheme largely relies on the success of probabilistic
approaches to NLP. Secondly, the scheme has been illustrated with two
fundamental applications in NLP: tagging, i.e. the assignment of lexical
categories to words, and parsing, i.e. the determination of the syntactic
structure of sentences. In both cases, the elements of the evolutionary
algorithm are described in detail, as well as the results of different
experiments carried out to show the viability of this evolutionary approach
to deal with tasks as complex as those of NLP.