A framework for multi-model EDAs with model recombination

作者: Thomas Weise , Stefan Niemczyk , Raymond Chiong , Mingxu Wan

DOI: 10.1007/978-3-642-20525-5_31

关键词:

摘要: Abstract Estimation of Distribution Algorithms (EDAs) are evolutionary optimization methods that build models which estimate the distribution of promising regions in the search space …

参考文章(25)
Martin Pelikan, Erick Cantú-Paz, Kumara Sastry, Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence) Springer-Verlag New York, Inc.. ,(2006)
Marcus Frean, Tom Downs, Marcus Gallagher, Real-valued evolutionary optimization using a flexible probability density estimator genetic and evolutionary computation conference. ,vol. 1, pp. 840- 846 ,(1999)
Rafał Sałustowicz, Jürgen Schmidhuber, Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space european conference on machine learning. ,vol. 1224, pp. 213- 220 ,(1997) , 10.1007/3-540-62858-4_86
Martin Pelikan, Kumara Sastry, Erick Cantú-Paz, Scalable optimization via probabilistic modeling : from algorithms to applications Springer. ,(2006)
Jose A. Lozano, Pedro Larraanaga, Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation Kluwer Academic Publishers. ,(2001)
Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga, Evolutionary computation based on Bayesian classifiers International Journal of Applied Mathematics and Computer Science. ,vol. 14, pp. 335- 349 ,(2004)
Hans-Georg Beyer, Hans-Paul Schwefel, Evolution strategies –A comprehensive introduction Natural Computing. ,vol. 1, pp. 3- 52 ,(2002) , 10.1023/A:1015059928466
Martin Pelikan, David E. Goldberg, Genetic Algorithms, Clustering, and the Breaking of Symmetry parallel problem solving from nature. pp. 385- 394 ,(2000) , 10.1007/3-540-45356-3_38