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We present a modular evolutionary framework, coined CASE for "complex adaptive system evolver", to automate the modeling and analysis of agent-based simulations (ABSs). The field of agent-based modeling is rapidly growing due to its capabilities to expose the emerging complex phenomena occurring in a wide range of natural and artificial systems such as biological cells, societies, battlefields, stock markets, etc. Nevertheless, studying agent-based simulations is a complicated, interdisciplinary and time-consuming process. Indeed, a large number of simulation parameters has to be considered to identify and fully understand the conditions leading to the emerging phenomena of interest. To tackle this difficulty, the study of ABSs is thus typically conducted in an iterative manner, where each iteration includes the successive and manual modeling of ABSs and analysis of simulation outcomes. To automate this …