Abstract
This paper explores the behavior of the multi–objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi–objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many–objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost.
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References
Ahn, C.W.: Advances in Evolutionary Algorithms. Theory, Design and Practice. Springer, Heidelberg (2006)
Bosman, P.A., Thierens, D.: The Naïve MIDEA: A baseline multi–objective EA. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 428–442. Springer, Heidelberg (2005)
Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Genetic and Evolutionary Computation. Springer, New York (2007), http://www.springer.com/west/home/computer/foundations?SGWI=4-156-22-173660344-0
Costa, M., Minisci, E., Pasero, E.: An hybrid neural/genetic approach to continuous multi–objective optimization problems. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds.) WIRN 2003. LNCS, vol. 2859, pp. 61–69. Springer, Heidelberg (2003)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, Advanced Information and Knowledge Processing, pp. 105–145. Springer, Heidelberg (2004)
Ehrgott, M.: Multicriteria Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 491. Springer, Heidelberg (2005)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)
Khare, V., Yao, X., Deb, K.: Performance Scaling of Multi-objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 376–390. Springer, Heidelberg (2003)
Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2006)
Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms. In: A new tool for Evolutionary Computation. Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)
Levon, J.: OProfile manual. Victoria University of Manchester (2004), http://oprofile.sourceforge.net/
Martí, L., García, J., Berlanga, A., Molina, J.M.: A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms. In: Thierens, D., Deb, K., Pelikan, M., Beyer, H.G., Doerr, B., Poli, R., Bittari, M. (eds.) Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (GECCO 2007, p. 911. ACM Press, New York (2007), http://portal.acm.org/citation.cfm?doid=1276958.1277141
Martí, L., García, J., Berlanga, A., Molina, J.M.: Introducing MONEDA: Scalable multiobjective optimization with a neural estimation of distribution algorithm. In: Thierens, D., Deb, K., Pelikan, M., Beyer, H.G., Doerr, B., Poli, R., Bittari, M. (eds.) GECCO 2008: 10th Annual Conference on Genetic and Evolutionary Computation, pp. 689–696. ACM Press, New York (2008); eMO Track “Best Paper” Nominee
Martí, L., García, J., Berlanga, A., Molina, J.M.: Model–building algorithms for multiobjective EDAs: Directions for improvement. In: Michalewicz, Z. (ed.) Computational Intelligence: Research Frontiers, pp. 2848–2855. IEEE Press, Piscataway (2008) doi:10.1109/CEC.2008.4631179
Martí, L., García, J., Berlanga, A., Molina, J.M.: Scalable continuous multiobjective optimization with a neural network–based estimation of distribution algorithm. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 535–544. Springer, Heidelberg (2008)
Martinetz, T.M., Berkovich, S.G., Shulten, K.J.: Neural–gas network for vector quantization and its application to time–series prediction. IEEE Transactions on Neural Networks 4, 558–560 (1993)
Pareto, V.: Cours D’Economie Politique. F. Rouge, Lausanne (1896)
Pelikan, M., Sastry, K., Goldberg, D.E.: Multiobjective estimation of distribution algorithms. In: Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Studies in Computational Intelligence, pp. 223–248. Springer, Heidelberg (2006)
Purshouse, R.C., Fleming, P.J.: On the evolutionary optimization of many conflicting objectives. IEEE Transactions on Evolutionary Computation 11(6), 770–784 (2007)
Qin, A.K., Suganthan, P.N.: Robust growing neural gas algorithm with application in cluster analysis. Neural Networks 17(8–9), 1135–1148 (2004)
Timm, H., Borgelt, C., Doring, C., Kruse, R.: An extension to possibilistic fuzzy cluster analysis. Fuzzy Sets and Systems 147(1), 3–16 (2004)
Zhang, Q., Zhou, A., Jin, Y.: RM–MEDA: A regularity model–based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12(1), 41–63 (2008)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailou, P., Fogarty, T. (eds.) EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2002)
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Martí, L., García, J., Berlanga, A., Molina, J.M. (2009). On the Computational Properties of the Multi-Objective Neural Estimation of Distribution Algorithm. In: Krasnogor, N., Melián-Batista, M.B., Pérez, J.A.M., Moreno-Vega, J.M., Pelta, D.A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008). Studies in Computational Intelligence, vol 236. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03211-0_20
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DOI: https://doi.org/10.1007/978-3-642-03211-0_20
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