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In this paper we present an approach to multi-objective exploration of the mapping space of a mesh-based network-on-chip architecture. Based on evolutionary computing techniques, the approach is an efficient and accurate way to obtain the Pareto mappings that optimize performance and power consumption. Integration of the approach in an exploration framework with a kernel based on an event-driven trace-based simulator makes it possible to take account of important dynamic effects that have a great impact on mapping. Validation on both synthesized traffic and real applications (an MPEG-2 encoder/decoder system) confirms the efficiency, accuracy and scalability of the approach.