Abstract
Extending Estimation of distribution algorithms (EDAs) to the continuous field is a promising and challenging task. With a single probabilistic model, most existing continuous EDAs usually suffer from the local stagnation or a low convergence speed. This paper presents an enhanced continuous EDA with multiple probabilistic models (MP-EDA). In the MP-EDA, the population is divided into two subpopulations. The one involved by histogram model is used to roughly capture the global optima, whereas the other involved by Gaussian model is aimed at finding highly accurate solutions. During the evolution, a migration operation is periodically carried out to exchange some best individuals of the two subpopulations. Besides, the MP-EDA adaptively adjusts the offspring size of each subpopulation to improve the searching efficiency. The effectiveness of the MP-EDA is investigated by testing ten benchmark functions. Compared with several state-of-the-art evolutionary computations, the proposed algorithm can obtain better results in most test cases.
This work was supported in part by the National Natural Science Foundation of China No. U0835002 and No.61070004, by the National High-Technology Research and Development Program (“863” Program) of China No. 2009AA01Z208.
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References
Baluja, S.: Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical report CMU-CS-94-163, Carnegie Mellon University (1994)
Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. binary parameters. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)
Sebag, M., Ducoulombier, A.: Extending population-based incremental learning to continuous search spaces. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 418–427. Springer, Heidelberg (1998)
Larrañaga, P., Etxeberria, R., Lozano, J.A., Peňa, J.M.: Optimization in continuous domains by learning and simulation of Gaussian networks. In: Proceedings of the Genetic and Evolutionary Computation Conference 2000, Las Vegas, Nevada (July 2000)
Paul, T.K., Iba, H.: Real-Coded Estimation of Distribution Algorithm. In: Proceedings of The Fifth Metaheuristics International Conference (2003)
Yuan, B., Gallagher, M.: Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA. In: Proc. of Congress on Evolutionary Computation (CEC 2005), vol. 2, pp. 1792–1799 (2005)
Lu, Q., Yao, X.: Clustering and learning Gaussian distribution for continuous optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(2), 195–204 (2005)
Tsutsui, S., Pelikan, M., Goldberg, D.E.: Evolutionary algorithm using marginal histogram models in continuous domain. IlliGAL Report No. 2001019, UIUC (2001)
Zhang, Q., Sun, J., Tsang, E., Ford, J.: Hybrid Estimation of Distribution Algorithm for Global Optimsation. Engineering Computations 21(1), 91–107 (2004)
Ding, N., Zhou, S., Sun, Z.: Histogram-based estimation of distribution algorithm: a competent method for continuous optimization. Computer Science and Technology 23(1), 35–42 (2008)
Xiao, J., Yan, Y.P., Zhang, J.: HPBILc: A histogram-based EDA for continuous optimization. Applied Mathematics and Computation 215(3), 973–982 (2009)
Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. on Evolutionary Computation 10(3), 280–295 (2006)
Storn, R.M., Price, K.V.: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11, 341–359 (1997)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. On Evolutionary Computation 3(2), 82–102 (1999)
Auger, A., Hansen, N.: Performance evaluation of an advanced local search evolutionary algorithm. In: Proc. of IEEE Congress on Evolutionary Computation (CEC 2005), vol. 2, pp. 1777–1784 (September 2005)
yuan, B., Gallagher, M.: On the importance of diversity maintenance in estimation of distribution algorithms. In: Proc. of the Genetic and Evolutionary Computation Conference-GECCO-2005, pp. 719–726. ACM, New York (2005)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.-P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Nanyang Technol. Univ., Singapore, IIT Kanpur, India, KanGAL Rep. 2005005 (May 2005)
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Transactions on Evolutionary Computation 12(1), 107–125 (2008)
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Zhong, Jh., Zhang, J., Fan, Z. (2010). MP-EDA: A Robust Estimation of Distribution Algorithm with Multiple Probabilistic Models for Global Continuous Optimization. In: Deb, K., et al. Simulated Evolution and Learning. SEAL 2010. Lecture Notes in Computer Science, vol 6457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17298-4_9
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