In the building automation domain, many prefabricated devices from different manufacturers available in the market realize building automation functions by preprogrammed software components. For given design requirements, the existence of a high number of devices that realize the required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which we approach with a multi-objective evolutionary algorithm. By integrating problemspecific knowledge into variation operations, a promisingly high optimization performance can be achieved. To realize this, diverse variation operations related to goals are defined upon a classification for the exploration and convergence behavior, and applied in different strategies.
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