  This paper presents an automatic method for data classification in nuclear
physics experiments based on evolutionary computing and vector quantization.
The major novelties of our approach are the fully automatic mechanism and the
use of analytical models to provide physics constraints, yielding to a fast and
physically reliable classification with nearly-zero human supervision. Our
method is successfully validated by using experimental data produced by stacks
of semiconducting detectors. The resulting classification is highly
satisfactory for all explored cases and is particularly robust to noise. The
algorithm is suitable to be integrated in the online and offline analysis
programs of existing large complexity detection arrays for the study of
nucleus-nucleus collisions at low and intermediate energies.
