Abstract
In this paper, we generalize the models used by MacKay [1] in his analysis of evolutionary strategies that are based on sexual, rather than asexual, reproduction methods. This analysis can contribute to the understanding of the relative power of genetic algorithms over search methods based upon stochastic hill-climbing, e.g. [2], [3].
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
MacKay, D.J.C.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Mitchell, M., Holland, J.H., Forrest, S.: When will a genetic algorithm outperform hill climbing. In: Cowan, J.D., Tesauro, G., Alspector, J. (eds.) Advances in Neural Information Processing Systems, vol. 6, pp. 51–58. Morgan Kaufmann Publishers, Inc., San Francisco (1994)
Baum, E.B., Boneh, D., Garrett, C.: Where genetic algorithms excel. In: Proceedings COLT 1995, Santa Cruz, California (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Andrews, M.W., Salzberg, C. (2004). Sexual and Asexual Paradigms in Evolution: The Implications for Genetic Algorithms. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-24855-2_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
eBook Packages: Springer Book Archive