We present an approach to test self-organizing emergent systems for unwanted behavior with respect to inefficiencies in task fulfillment based on evolutionary learning of event sequences. By using the differences in produced solution quality versus optimal quality to guide the evolutionary search and by using in addition to standard evolutionary operators targeted ones reflecting knowledge about the tested system, the usual evolutionary learning effects can take place, leading to event

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... that are solved badly by the tested systems. In our experimental evaluation of 2 variants of a self-organizing emergent system for dynamic pickup-and-delivery problems, a system using our learning testing approach created clear evidence that the basic variant of the tested system has problems regarding the efficiency of the solutions it produces and that the efficiency improved version leads even in an extremely negative setting for it to only about double the quality costs