Convergence is a necessary part of any successful GP run, but is also a great weakness due to material lost through the hemorrhage of genetic content through the established evolutionary dynamics. This chapter examines the phenomenon of convergence commonly observed in evolutionary systems before introducing a number of functionally distinct mechanisms are analysed and tested with respect to their ability to modulate the loss of potentially useful genetic content. Each of these methods use little or no explicit measurements to calculate diversity, and we show that they can have a dramatic effect on empirical performance of GP.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Braught, G.W. (2005). Learning and lineage selection in genetic algorithms. In IEEE Transactions on Evolutionary Computation. IEEE Computational Intelligence Society.
D. Beasley, D. R. Bull and Martin, R. R. (1993). A sequential niche technique for multimodal function optimization. In Evoluionary Computation, volume 1, pages 101-125.
Darwen, P.J. and Yao, Xin (1997). Speciation as automatic categorical modularization. In IEEE Transactions on Evolutionary Computation, volume 1. IEEE Computational Intelligence Society.
Dehmeshki, J., Siddique, M., Lin, Xin-Yu, Roddie, M., and Costello, J. (2003). Automated detection of nodules in the ct lung images using multi-modal genetic algorithm. In IEEE Transactions on Evolutionary Computation, volume 1. IEEE Computational Intelligence Society.
Goldberg, D. E. and Richardson, J (1987). Genetic algorithms with sharing for multimodal function optimisation. In Proc. 2nd Int Conf. Genetic Algorithms, pages 41-49.
Holland, John .H. (1975). Adaptation in Natural and Artificial Systems. MIT Press.
Hornby, Gregory S. (2005). Alps: the age-layered population structure for reducing the problem of premature convergence. In Gecco 2005.
Hu, Jianjun and Goodman, Erik D. (2002). The hierarchical fair competition (HFC) model for parallel evolutionary algorithms. In Fogel, David B., ElSharkawi, Mohamed A., Yao, Xin, Greenwood, Garry, Iba, Hitoshi, Marrow, Paul, and Shackleton, Mark, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 49-54. IEEE Press.
Keijzer, Maarten, Ryan, Conor, Murphy, Gearoid, and Cattolico, Mike (2005). Undirected training of run transferable libraries. In Keijzer, Maarten, Tettamanzi, Andrea, Collet, Pierre, van Hemert, Jano I., and Tomassini, Marco, editors, Proceedings of the 8th European Conference on Genetic Programming, volume 3447 of Lecture Notes in Computer Science, pages 361-370, Lausanne, Switzerland. Springer.
Koza, John R. (1994).Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts.
Naoyuki Kubota, Toshio Fukuda, Fumihito Arai and Shimojima, Koji (1994). Genetic algorithm with age structure and its application to self organising manufacturing system. In IEEE Symposium on Emerging Technologies and Factory Automation.
Sareni, Bruno and Krahenbuhl, Laurent (1998). Fitness sharing and niching methods revisited. In IEEE Transactions on Evolutionary Computation, volume 2. IEEE Computational Intelligence Society.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Murphy, G., Ryan, C. (2008). Manipulation of Convergence in Evolutionary Systems. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice V. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76308-8_3
Download citation
DOI: https://doi.org/10.1007/978-0-387-76308-8_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-76307-1
Online ISBN: 978-0-387-76308-8
eBook Packages: Computer ScienceComputer Science (R0)