Dynamic irregular cellular learning automata

Irregular Cellular learning automaton (ICLA), which is recently introduced, is a cellular learning automaton (CIA) with irregular structure. ICLA is suitable for modeling problems which are not regular in nature, such as problems in the area of sensor networks, web mining, and grid computing. In some areas such as mobile ad hoc and sensor networks, where the structure of the environment changes over the time, an ICLA with a dynamic structure is required. For this reason, in this paper, we have extended ICLA in such a way that the structure of the extended model, called dynamic ICLA (DICLA), can change over time. For the newly introduced model, we have proposed the concept of expediency and then, discussed sufficient conditions under which a DICLA becomes expedient. (C) 2017 Elsevier B.V. All rights reserved.