Abstract
This chapter shows experimental results of applying continuous Estimation of Distribution Algorithms to some well known optimization problems. For this UMDAC, MIMICe, EGNABIc, EGNABGe, EGNAee, EMNAglob, 1, and EMNAa algorithms were implemented. Their performance was compared to such of Evolution Strategies (Schwefel, 1995). The optimization problems of choice were Summation cancellation, Griewangk, Sphere model, Rosenbrock generalized, and Ackley.
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
Ackley, D. H. (1987). A connectionist machine for genetic hillclimbing. Kluwer, Boston.
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press.
Baluja, S. and Davies, S. (1997). Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. Technical report, Carnegie Mellon Report, CMU-CS-97–107.
Larrañaga, P., Etxeberria, R., Lozano, J., and Peña, J. (2000). Optimization in continuous domains by learning and simulation of Gaussian networks. In Proceedings of the Workshop in Optimization by Building and using Probabilistic Models. A Workshop within the 2000 Genetic and Evolutionary Computation Conference, GECCO 2000, pages 201–204, Las Vegas, Nevada, USA.
Larrañaga, P., Lozano, J. A., and Bengoetxea, E. (2001). Estimation of Distribution Algorithms based on multivariate normal and Gaussian networks. Technical Report KZZA-IK-1–01, Department of Computer Science and Artificial Intelligence, University of the Basque Country.
Rosenbrock, H. H. (1960). An automatic method for finding the greatest or least value of a function. The Computer Journal, 3:175–184.
Salomon, R. (1998). Evolutionary algorithms and gradient search: similarities and differences. IEEE Transactions on Evolutionary Computation, 2(2):4555.
Schwefel, H.-P. (1995). Evolution and Optimum Seeking. Wiley InterScience. Törn, A. and Zilinskas, A. (1989). Global optimization. Lecture Notes in Computer Science 350. Springer—Verlag, Berlin Heidelberg.
Whitley, D. and Kauth, J. (1988). GENITOR: A different genetic algorithm. In Proceedings of the Rocky Mountain Conference on Artificial Intelligence, volume II, pages 118–130.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Science+Business Media New York
About this chapter
Cite this chapter
Bengoetxea, E., Miquélez, T., Larrañaga, P., Lozano, J.A. (2002). Experimental Results in Function Optimization with EDAs in Continuous Domain. In: Larrañaga, P., Lozano, J.A. (eds) Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1539-5_8
Download citation
DOI: https://doi.org/10.1007/978-1-4615-1539-5_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-5604-2
Online ISBN: 978-1-4615-1539-5
eBook Packages: Springer Book Archive