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Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other hand, they need to achieve high (real-time) performance. This wide spectrum of design requirements leads to complex heterogeneous system-on-chip (SoC) architectures. The complexity of embedded systems forces designers to model and simulate systems and their components to explore the wide range of design choices. Such design space exploration is especially needed during the early design stages, where the design space is at its largest. Due to the exponential design space in real problems and multiple criteria to be considered, multi-objective evolutionary algorithms (MOEAs) are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. Interpreting the search results (e.g., where are the Pareto points located), understanding their relations and analyzing how the design space was searched by such searching algorithms is of invaluable importance to the designer. To this end, this paper presents a novel interactive visualization tool, based on tree visualization, to understand the search dynamics of a MOEA and to visualize where the optimum design points are located in the design space and what objective values they have.