Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a evolutionary algorithm framework which easily adapts to a large number of statistical settings. The experiments on artificial and real-world time series show that the algorithm detects break points with high precision and is computationally very efficient.   A reference implementation is availble at the following address: http://www2.imm.dtu.dk/~pafi/SBX/launch.html}
['Benjamin Doerr', 'Paul Fischer', 'Astrid Hilbert', 'Carsten Witt']
Evolutionary algorithms for the detection of structural breaks in time series: extended abstract