The emerging science of simulation enables us to explore the dynamics of large and complex systems even if a formal representation and analysis of the system is intractable and a construction of a real-world instantiation for the purpose of experimentation is too expensive. A computer simulation model can be run for many more configurations and the accumulated observations deepen our understanding of the system's operation, but it is very important that we have tools that help us manage the

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... numbers of experiments that need to be run and the massive data sets that are collected. Furthermore, as we explore vast parameter spaces of simulation model, we need guidance in finding regions of interest in a resource efficient way. In this paper we use a model of agent-based graph coloring to introduce a software infrastructure for the systematic execution of experiments across large regions of parameter space (parameter sweep). Furthermore, we present a multi-agent system that searches large parameter spaces automatically for regions of interest specified by a fitness function. The fitness function captures the researcher's interest in certain system dynamics. We specify a function that searches for overlap regions that accompany phase changes in the simulation model. The agents search the parameter space by executing simulation experiments in regions of high fitness. As a consequence, the use of computational resources is minimized.