Abstract
Many problems can be characterized by several competing objectives. Multiple objective optimization problems have recently received considerable attention specially by the evolutionary algorithms community. Their proposals, however, require an adequate codification of the problem into strings, which is not always easy to do. This paper introduces a new algorithm, called MDQL, for multiple objective optimization problems which does not suffer from previous limitations. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. Furthermore, an extension for applying reinforcement learning to continuos functions is also given. Successful results of MDQL on a continuos non restricted problem whose Pareto front is convex and on a continuos non-convex problem with restrictions are described.
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References
Bilchev, G., Parmee, I.: The Ant Colony Methaphor for Searching Continuous Design Spaces. In: Fogarty, T.C. (ed.) AISB-WS 1995. LNCS, vol. 993, pp. 25–39. Springer, Heidelberg (1995)
Bilchev, G., Parmee, I.: Constrained optimization with an Ant Colony search model. In: Proceedings of ACEDC 1996, PEDC, University of Plymouth, UK, March 26-28 (1996)
Fonseca, C.M., Flemming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1–16 (1995)
Fonseca, C.M., Flemming, P.J.: Multiobjective optimization and Multiple Constraint Handling with Evolutionary Algorithms I: A Unified Formulation. Research Report 564, Dept. Automatic Control and Systems Eng., University of Sheffield. Sheffield S1 4Du, U.K. (January 1995)
Gambardella, L.M., Dorigo, M.: Ant-Q: A Reinforcement Learning approach to the traveling salesman problem. In: Proceedings of the 12th. International Machine Learning Conference (ML 1995), pp. 252–260. Morgan Kaufmann, San Francisco (1995)
Knowles, J., Corne, D.: Assesing the Performance of the Pareto Achieved Evolution Strategy. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program, Orlando, Florida USA, pp. 123–124. Morgan Kaufmann, San Francisco (1999)
Mariano, C., Morales, E.: MOAQ an Ant-Q Algorithm for Multiple Objective Optimization Problems. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida USA, pp. 894–901. Morgan Kaufmann, San Francisco (1999)
Mariano, C., Morales, E.: MOAQ a Distributed Reinforcement Learning Algorithm for the Solution of Multiple Objectives Optimization Problems. In: Memorias del Segundo Encuentro Nacional de Computación ENC 1999, paper No. 111. September 12-15, Pachuca Hidalgo, Mexico, Sociedad Mexicana de Ciencias de la Computación (1999)
Schaffer, D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Grefenstette, J.J. (ed.) Proccedings of the First International Conference on Genetic Algorithms, pp. 93–100. Lawrence Erlbaum, Hillsdale (1985)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Valenzuela-Rendón, M., Uresti-Charre, E.: A non-generational genetic algorithm for multiobjective optimization. In: Proceedings of the Seventh International conference on Genetic Algorithms, California, pp. 658–665. Morgan Kaufmann, San Francisco (1997)
van Veldhizen, D., Lemont, G.: MOEA Test Suite Generation, Design & Use. Research Report, Department of Electrica and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Ohio (June 1999)
Watkins, C.J.C.H.: Learning from Delayed Rewards. Ph.D. thesis, Cambridge University (1989)
Yuret, D.: From Genetic Algorithms to Efficient Optimization, Msc Thesis in Electrical Engineering and Computer Science, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (May 1994)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Proceedings of the Parallel problem Solving from Nature- PPSN V, Amsterdam The Netherlands, pp. 292–301. Springer, Heidelberg (September 1998)
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Mariano, C., Morales, E. (2000). A New Approach for the Solution of Multiple Objective Optimization Problems Based on Reinforcement Learning. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_20
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DOI: https://doi.org/10.1007/10720076_20
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