Abstract
In this paper, a new algorithm named SGEGC was proposed. Inspired by selfish gene theory, SGEGC uses a vector of survival rate to model the condition distribution, which serves as a virtual population that is used to generate new individuals. While the present Estimation of Distribution Algorithms (EDAs) require much time to learn the complex relationships among variables, SGEGC employs an approach that exchanges the relevant genetic components. Experimental results show that the proposed approach is more efficient in convergent reliability and convergent velocity in comparison with BMDA, COMIT and MIMIC in the test functions.
This is a preview of subscription content, log in via an institution to check access.
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
Ahn, C.W., Ramakrishna, R.S.: On the scalability of real-coded bayesian optimization algorithm. IEEE Transactions on Evolutionary Computation 12(3), 307–322 (2008)
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 Pittsburgh, PA, USA (1994)
Baluja, S., Davies, S.: Using optimal dependency-trees for combinational optimization. In: ICML 1997: Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, CA, USA, pp. 30–38 (1997)
Baluja, S., Davies, S.: Fast probabilistic modeling for combinatorial optimization. In: Proc. of 15th National Conf. on Artificial Intelligence(AAAI), pp. 469–476 (1998)
Bonet, J., Isbell, C.L., Viola, P.: Mimic: Finding optima by estimating probability densities. In: Advances in Neural Information Processing Systems, vol. 9, pp. 424–430. MIT Press, Cambridge (1997)
Corno, F., Reorda, M.S., Squillero, G.: A new evolutionary algorithm inspired by the selfish gene theory. In: ICEC 1998: IEEE International Conference on Evolutionary Computation, pp. 575–580 (1998)
Corno, F., Reorda, M., Squillero, G.: The selfish gene algorithm: a new evolutionary optimization strategy. In: SAC 1998: 13th Annual ACM Symposium on Applied Computing, pp. 349–355 (1998)
Corno, F., Reorda, M., Squillero, G.: Exploiting the selfish gene algorithm for evolving cellular automata. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000, vol. 6, pp. 577–581 (2000)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Transactions on Evolutionary Computation 3(4), 287–297 (1999)
Muhlenbein, H., Paass, G.: From recombination of genes to the estimation of distributions i. binary parameters. In: PPSN IV: Proceedings of the 4th International Conference on Parallel Problem Solving from Nature, London, UK, pp. 178–187 (1996)
Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Boston (2002)
Muhlenbein, H., Mahnig, T.: The factorized distribution algorithm for additively decomposedfunctions. In: Proceedings of the 1999 Congress on Evolutionary Computation,1999.CEC 1999, vol. 1 (1999)
Pelikan, M., Goldberg, D.E., Cant-Paz, E.: Boa: The bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference(GECCO-1999), pp. 525–532. Morgan Kaufmann, San Francisco (1999)
Pelikan, M., Muhlenbein, H.: The bivariate marginal distribution algorithm. In: Advances in Soft Computing:Engineering Design and Manufacturing, pp. 521–535. Springer, London (1999)
Dawkins, R.: The Selfish Gene - new edition. Oxford University Press, Oxford (1989)
Harik, G.R., Lobo, F.G., Sastry, K.: Linkage learning via probabilistic modeling in the extended compact genetic algorithm(ecga). Scalable Optimization via Probabilistic Modeling, 39–61 (2006)
Zhou, S.D., Sun, Z.Q.: A survey on estimation of distribution algorithms. Acta Automatica Sinica 33(2), 113–124 (2007)
Yang, S.Y., Ho, S.L., Ni, G.Z., Machado, J.M., Wong, K.F.: A new implementation of population based incremental learning method for optimizations in electromagnetics. IEEE Transactions on Magnetics 43(4), 1601–1604 (2007)
Yang, S.X., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation 12(5), 542–561 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, C., Li, Y., Lin, Z. (2009). SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-04843-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
eBook Packages: Computer ScienceComputer Science (R0)