  We address the problem of automatic anomaly detection in high energy collider
data. Our approach is based on the random generation of analytic expressions
for kinematical variables, which can then be evolved following a genetic
programming procedure to enhance their discriminating power. We apply this
approach to three concrete scenarios to demonstrate its possible usefulness,
both as a detailed check of reference Monte-Carlo simulations and as a model
independent tool for the detection of New Physics signatures.
