Abstract
Many problems formerly considered intractable have been satisfactorily resolved using approximate optimization methods called metaheuristics. These methods use a non-deterministic approach that finds good solutions, despite not ensuring the determination of the overall optimum. The success of a metaheuristic is conditioned on its capacity of alternating properly between the exploration and exploitation of solution spaces. During the process of searching for better solutions, a metaheuristic can be guided to regions of promising solutions using the acquisition of information on the problem under study. In this study this is done through the use of reinforcement learning. The performance of a metaheuristic can also be improved using multiple search trajectories, which act competitively and/or cooperatively. This can be accomplished using parallel processing. Thus, in this paper we propose a hybrid parallel implementation for the GRASP metaheuristics and the genetic al gorithm, using reinforcement learning, applied to the symmetric traveling salesman problem.
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References
White, A., Mann, J., Smith, G.: Genetic algorithms and network ring design. Annals of Operational Research 86, 347–371 (1999)
Darrell, W.: A genetic algorithm tutorial. Statistics and Computing 4(2), 65–85 (1994)
Fang, H.: Genetic algorithms in timetabling and scheduling. PhD thesis, Department of Artificial Intelligence. University of Edinburgh, Scotland (1994)
Feo, T., Resende, M.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109–133 (1995)
Foster, I.: Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Addison-Wesley Longman Publishing Co., Inc., Boston (1995)
Karp, R.: On the computational complexity of combinatorial problems. Networks 5, 45–68 (1975)
Lima Junior, F.C., Melo, J.D., Doria Neto, A.D.: Using q-learning algorithm for initialization of the GRASP metaheuristic and genetic algorithm. In: IEEE International Joint Conference on Neural Networks, ITE - ISPEC, Orlando, FL, USA, pp. 1243–1248 (2007)
Prais, M., Ribeiro, C.C.: Reactive grasp: An application to a matrix decomposition problem in tdma traffic assignment. Jornal on Computing 12(3), 164–176 (2000)
Randy, H., Haupt, S.E.: Patrical Genetic Algoritms, 2nd edn. Wiley Intercience, Chichester (1998)
Reinelt, G.: The Traveling Salesman: Computational Solutions for TSP Applications, vol. 840, pp. 187–199. Springer, Heidelberg (1994)
Resende, M., Ribeiro, C.: GRASP with Path-relinking: Recent Advances and Applications, pp. 29–63. Springer, Heidelberg (2005)
Resende, M.G.C., de Sousa, J.P., Viana, A. (eds.): Metaheuristics: computer decision-making. Kluwer Academic Publishers, Norwell (2004)
Ribeiro, C.: Essays and Surveys in Metaheuristics. Kluwer Academic Publishers, Norwell (2002)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Thangiah, S.: Vehicle Routing with Time Windows using Genetic Algorithms. In: Application Handbook of Genetic Algorithms, New Frontiers, vol. II, pp. 253–277 (1995)
Vázquez, M., Whitley, L.D.: A comparison of genetic algorithms for the static job shop scheduling problem. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 303–312. Springer, Heidelberg (2000)
Watkins, C.: Learning from delayed rewards. PhD thesis, University of Cambridge, England (1989)
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dos Santos, J.P.Q., de Lima Júnior, F.C., Magalhães, R.M., de Melo, J.D., Neto, A.D.D. (2010). A Parallel Hybrid Implementation Using Genetic Algorithms, GRASP and Reinforcement Learning for the Salesman Traveling Problem. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Expensive Optimization Problems. Adaptation Learning and Optimization, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10701-6_14
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DOI: https://doi.org/10.1007/978-3-642-10701-6_14
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