This paper presents an evolutionary algorithm for modeling the arrival
dates of document streams, which is any time-stamped collection of
documents, such as newscasts, e-mails, scientific journals archives and
weblog postings. The goal is to find a frequency curve that fits the data
circumventing the unavoidable noise. Classical dynamic programming
algorithms are limited by memory and efficiency requirements, which
can be a problem when dealing with long streams. This suggests to
explore alternative search methods which although do not guarantee optimality,
are far more efficient. Experiments have shown that the designed evolutionary
algorithm is able to reach high quality solutions in a short time.
We have also explored different approaches to infer whether new arrivals
increase or decrease {\em interest} in the topic the document stream is about.
In particular, we present a variant of the evolutionary algorithm, which is
able to very quickly fit a stream extended with new data, by taking advantage
of the fit obtained for the original substream. These mechanisms can be used
for real time detection of changes in the trend of interest in a topic, an
important application of this kind of models.