Deterministic learning automata solutions to the equipartitioning problem

Three deterministic learning automata solutions to the problem of equipartitioning are presented. Although the first two are ε-optimal, they seem to be practically feasible only when a set of W objects is small. The last solution, which uses a novel learning automaton, demonstrates an excellent partitioning capability. Experimentally, this solution converges an order of magnitude faster than the best known algorithm in the literature