  Tasks related to Natural Language Processing (NLP) have recently been the
focus of a large research endeavor by the machine learning community. The
increased interest in this area is mainly due to the success of deep learning
methods. Genetic Programming (GP), however, was not under the spotlight with
respect to NLP tasks. Here, we propose a first proof-of-concept that combines
GP with the well established NLP tool word2vec for the next word prediction
task. The main idea is that, once words have been moved into a vector space,
traditional GP operators can successfully work on vectors, thus producing
meaningful words as the output. To assess the suitability of this approach, we
perform an experimental evaluation on a set of existing newspaper headlines.
Individuals resulting from this (pre-)training phase can be employed as the
initial population in other NLP tasks, like sentence generation, which will be
the focus of future investigations, possibly employing adversarial
co-evolutionary approaches.
