Given a graph G, we intend to partition its nodes into two sets of equal size so as to minimize the sum of the cost of the edges having end-points in different sets. This problem, called the uniform graph partitioning problem, is known to be NP-Complete. In this paper we propose the first reported learning-automaton based solution to the problem. We compare this new solution to various reported schemes such as the Kernighan-Lin's algorithm, and two excellent recent heuristic methods proposed by Rolland et al. - an extended local search algorithm and a genetic algorithm. The current automaton-based algorithm outperforms all the other schemes. We believe that it is the fastest algorithm reported to date. Additionally, our solution can also be adapted for the GPP (See note at end of Section 1) in which the edge costs are not constant but random variables whose distributions are unknown.

Additional Metadata
Keywords Adaptive learning, Graph partitioning, Heuristic search, Learning automata
Persistent URL dx.doi.org/10.1109/12.485372
Journal IEEE Transactions on Computers
Citation
Oommen, J, & De St. Croix, E.V. (Edward V.). (1996). Graph partitioning using learning automata. IEEE Transactions on Computers, 45(2), 195–208. doi:10.1109/12.485372