This paper presents an evolutionary algorithm for modeling the arrival dates in time-stamped data
sequences such as newscasts, e-mails, IRC conversations, scientific journal articles or weblog postings.
These models are applied to the detection of buzz (i.e. terms that occur with a higher-than-normal
frequency) in them, which has attracted a lot of interest in the online world with the increasing number of
periodic content producers. That is why in this paper we have used this kind of online sequences to test
our system, though it is also valid for other types of event sequences. The algorithm assigns frequencies
(number of events per time unit) to time intervals so that it produces an optimal fit to the data. The
optimization procedure is a trade off between accurately fitting the data and avoiding too many frequency
changes, thus overcoming the noise inherent in these sequences. This process has been traditionally performed
using dynamic programming algorithms, which are limited by memory and efficiency requirements. This limitation
can be a problem when dealing with long sequences, and suggests the application of alternative search methods
with some degree of uncertainty to achieve tractability, such as the evolutionary algorithm proposed in this
paper. This algorithm is able to reach the same solution quality as those classical dynamic programming
algorithms, but in a shorter time. We also test different cost functions and propose a new one that yields
better fits than the one originally proposed by Kleinberg on real-world data. Finally, several distributions
of states for the finite state automata are tested, with the result that an uniform distribution produces
much better fits than the geometric distribution also proposed by Kleinberg. We also present a variant of
the evolutionary algorithm, which achieves a fast fit of a sequence extended with new data, by taking
advantage of the fit obtained for the original subsequence.