Let a = {A,• • •, Aw} be a set of W objects to be equipartitioned into R classes {P1,...,pr}.the objects are accessed in groups, their joint access probabilities being unknown. The intention is that the objects accessed more frequently together are located in the same class. This problem has been known to be NP-hard [130]. In this paper, we propose three deterministic learning automata solutions to the problem. Although the first two are optimal, they seem to be practically feasible only when W is small. The last solution, which uses a new learning automaton, demonstrates an excellent partitioning capability. Experimentally, this solution converges an order of magnitude faster than the best known algorithm in the literature [30].

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Keywords Adaptive learning, attribute partitioning, au-, distributed file allocation, object partitioning problem, record clustering, tomata solutions to partitioning
Persistent URL dx.doi.org/10.1109/12.75146
Journal IEEE Transactions on Computers
Oommen, J, & Ma, D.C.Y. (Daniel C.Y.). (1988). Deterministic Learning Automata Solutions to the Equipartitioning Problem. IEEE Transactions on Computers, 37(1), 2–13. doi:10.1109/12.75146