@article {24485,
	title = {Solving Multiconstraint Assignment Problems Using Learning Automata},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) },
	volume = {40},
	year = {2010},
	pages = {6-18},
	publisher = {IEEE},
	doi = {10.1109/TSMCB.2009.2032528},
	author = {Horn, Geir and Oommen, John}
}
@inproceedings {Horn.2005.1,
	title = {A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem},
	journal = {Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2005)},
	year = {2005},
	month = {April},
	pages = {297b-297b},
	publisher = {IEEE},
	type = {1},
	address = {Denver, Colorado, USA, April 3-8},
	abstract = {This paper considers the problem of distributing the processes of a parallel application onto a set of computing nodes. This problem called the Static Mapping Problem (SMP) is known to be NP-Hard, and has been tackled using heuristic solutions. The objective of this paper is to present the first reported Learning Automaton (LA) based solution to the SMP, generated by the close resemblance of the SMP to the equipartitioning problem. The LA in question is of the so-called Fixed-Structure family, solution to the equipartitioning problem is then modified to solve the SMP. Several algorithmic variants of this solution have been implemented, and these have all been rigorously tested and evaluated through extensive simulations on randomly generated parallel applications. The focus in this work is to demonstrate the applicability of LA to the SMP, not to optimise and evaluate the performance of the proposed strategy. The results presented here clearly demonstrate that LA provide a promising tool that can effectively solve the mapping problem.},
	author = {Horn, Geir and Oommen, John},
	editor = {H. J. {Siegel} and David A. {Bader} and Gaudiot, Jean-Luc}
}
@inproceedings {Horn.2005.6,
	title = {Generalised Pursuit Learning Automata for Non-Stationary Environments Applied to the Stochastic Static Mapping Problem},
	journal = {The 11th International onference on Information Systems Analysis and Synthesis (ISAS 2005) and The 2nd International Conference on Cybernetics and Information Technologies, Systems and Applications (CITSA 2005)},
	year = {2005},
	note = {ISBN 980-6560-42-6},
	month = {July},
	pages = {91-97},
	publisher = {International Institute of Informatics and Systemics (IIIS)},
	type = {1},
	edition = {Volume 1},
	address = {Orlado, Florida, USA July 14-17},
	abstract = {In this paper, we propose the first variable-structure Learning-Automata (LA) based approach to solve the Stochastic Static Mapping Problem (SMP). The problem is known to be NP-hard and involves distributing the processes of a parallel application onto a set of computing nodes. Our solution has salient characteristics novel to both the fields of LA and process allocation. Thus, while the solution attempts to optimise the inter-process communication costs and the workload allocated to each machine, it achieves this without artificially generating potential pairings which are to be used by our previous fixed-structure LA-based solution. Furthermore, unlike all the reported estimator-based LA, we propose the utilization of the recently introduced weak estimators suitable for non-stationary environments. We report the results obtained by simulating our algorithm on various problems where the number of processes and nodes is "small". In each case, the accuracy of the strategy to converge to a feasible partitioning is remarkable - sometimes even as high as 96\%. We are currently investigating how the current solution can be enhanced to be rendered scalable.},
	author = {Horn, Geir and Oommen, John},
	editor = {Aguilar, Jos{\'e} and Chu, Hsing-Wei and St-Amand, Johnny and Galkin, Ivan and Chantzimisios, Periklis}
}
