Abstract
The Estimation of Distribution Algorithms(EDAs) is a new paradigm for Evolutionary Computation. This new class of algorithms generalizes Genetic Algorithms(GAs) by replacing the crossover and mutation operators by learning and sampling the probability distribution of the best individuals of the population at each iteration of the algorithm. In this paper, we review the EDAs for the solution of combinatorial optimization problems and optimization in continuous domains. The paper gives a brief overview of the multiobjective problems(MOP) and estimation of distribution algorithms(EDAs). We introduce a representative algorithm called RMMEDA (Regularity Model Based Multi-objective Estimation of Distribution Algorithm). In order to improve the convergence performance of the algorithm, we improve the traditional RM-MEDA. The improvement we make is using part of the parent population with better performance instead of the entire parent population to establish a more accurate manifold model, and the RM-MEDA based on elitist strategy theory is proposed. Experimental results show that the improved RM-MEDA performs better on the convergence metric and the algorithm runtime than the original one.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proc. 1st Int. Conf. Genetic Algorithms, Pittsburgh, PA, pp. 93–100 (1985)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Baffins Lane (2001)
Coello Coello, C.A., van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for solving Multi-Objective Problems. Kluwer, Norwell (2002)
Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Springer, Heidelberg (2005)
Knowles, J., Corne, D.: Memetic algorithms for multiobjective optimization: Issues, methods and prospects. In: Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol. 166, pp. 313–352. Springer, New York (2005)
Larranaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Norwell (2001)
Okabe, T., Jin, Y., Sendhoff, B., Olhofer, M.: Voronoi-based estimation of distribution algorithm for multi-objecbive optimization. In: Proc. Congr. Evol. Comput (CEC 2004), Portland, OR, pp. 1594–1601 (2004)
Bosman, P.A.N., Thierens, D.: The naive 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)
Pelikan, M., Sastry, K., Goldberg, D.: Multiobjective HBOA, clustering, and scalability. Illinois Genetic Algorithms Laboratory (IlliGAL), Tech. Rep. 2005005 (2005)
Cherkassky, V., Mulier, F.: Learning from Data: Concepts. Theory. and Methods. Wiley, New York (1998)
Hasite, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediciton. Springer, Berlin (2001)
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)
Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized differential evolution. In: Proc. Congr. Evol. Comput (CEC 2005), Edinburgh, U.K., pp. 443–450 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyaarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer’s International Series in Operations Research & Management Science, vol. 12. Kluwer, Norwell (1999)
Schutze, O., Mostaghim, S., Dellnitz, M., Teich, J.: Covering Pareto sets by multilevel evolutionary subdivision techniques. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 118–132. Springer, Heidelberg (2003)
Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genitic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans. Evol. Comput. 7, 204–223 (2003)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Evolutionary Multiobjective Optimization, Theoretical Advances and Applications, pp. 105–145. Springer, New York (2005)
Li, H., Zhang, Q.: A multiobjective differential evolution based on decomposition for multiobjective optimization with variable linkages. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 583–592. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mo, L., Dai, G., Zhu, J. (2010). The RM-MEDA Based on Elitist Strategy. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-16493-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
eBook Packages: Computer ScienceComputer Science (R0)