An evolutionary algorithm for the off-line data driven generation of fuzzy controllers for intelligent buildings

Ambient intelligence is nowadays an active research field. As a key matter of this concept, several approaches have been proposed for the development of learning architectures for the control of the devices in an intelligent building. In this paper, an evolutionary algorithm is analyzed as a candidate for the initial phases of the design of such architectures: fuzzy controllers for the devices are offline induced from data sampled from the environment. We would show results obtained using real data gathered from the Essex intelligent dormitory. The proposed algorithm seems to be suited for the task, both due to its accuracy and for the easy and meaningful linguistic interpretation of the solutions it produces