We introduce a new model for detection and tracking of bursts of events in a discrete temporal
sequence, its only requirement being that the time scale of events is long enough to make a discrete
time description meaningful. A model for the occurrence of events using with Poisson distributions
is proposed, which, applying Bayesian inference transforms into the well-known Potts model of
Statistical Physics, with Potts variables equal to the Poisson parameters (frequencies of events).
The problem then is to find the configuration that minimizes the Potts energy, what is achieved by
applying an evolutionary algorithm specially designed to incorporate the heuristics of the model.
We use it to analyze data streams of very different nature, such as seismic events and weblog
comments that mention a particular word. Results are compared to those of a standard dynamic
programming algorithm (Viterbi) which finds the exact solution to this minimization problem. We
find that, whenever both methods reach a solution, they are very similar, but the evolutionary
algorithm outperforms Viterbi's algorithm in running time by several orders of magnitude, yielding
a good solution even in cases where Viterbi takes months to complete the search.