  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, IRC conversations, scientific journals archives and weblog
postings. This algorithm assigns frequencies (number of document arrivals per
time unit) to time intervals so that it produces an optimal fit to the data.
The optimization is a trade off between accurately fitting the data and
avoiding too many frequency changes; this way the analysis is able to find fits
which ignore the 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 allow
for some degree of uncertainty to achieve tractability. Experiments have shown
that the designed evolutionary algorithm is able to reach the same solution
quality as those classical dynamic programming algorithms in a shorter time. We
have also explored different probabilistic models to optimize the fitting of
the date streams, and applied these algorithms to infer whether a new arrival
increases or decreases {\em interest} in the topic the document stream is
about.

Information Retrieval;Neural and Evolutionary Computing