Abstract
In this paper we compare and analyze the various diversity measures used in the Evolutionary Computation field. While each measure looks quite different from the others in form, we surprisingly found that the same basic method underlies all of them: the distance between all possible pairs of chromosomes/organisms in the population. This is true even of the Shannon entropy of gene frequencies. We then associate the different varieties of EC diversity measures to different diversity measures used in Biology. Finally we give an O(n) implementation for each of the diversity measures (where n is the population size), despite their basis in an O(n 2) number of comparisons.
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
Hutter M.: Fitness Uniform Selection to Preserve Genetic Diversity. In: Fogel D.B. (ed.): CEC’02. IEEE Press (2002) 783–788
Motoki, T.: Calculating the Expected Loss of Diversity of Selection Schemes. In Evolutionary Computation 10-4. MIT Press, Cambridge MA (2002) 397–423
Ang, K.H., Chong, G. and Li, Y.: Preliminary Statement on the Current Progress of Multi-Objective Evolutionary Algorithm Performance Measurement. In: Fogel D.B. (ed.): CEC’ 02. IEEE Press (2002) 1139–1144
Oppacher F. and M. Wineberg (1999). The Shifting Balance Genetic Algorithm: Improving the GA in a Dynamic Environment. In Banzhaf W. et al. (Eds.): GECCO’99. Morgan Kaufmann, San Francisco (1999) 504–510
Garrett, S.M., Walker, J.H.: Genetic Algorithms: Combining Evolutionary and ‘Non’-Evolutionary Methods in Tracking Dynamic Global Optima. In Langdon W. B. et. al. (Eds.): GECCO-2002. Morgan Kaufmann, San Francisco (2002) 359–366
Louis, S.J. and Rawlins, G.J.E.: Syntactic Analysis of Convergence in Genetic Algorithms. In Whitley, L.D. (ed.); Foundations of Genetic Algorithms 2. Morgan Kaufmann, San Mateo, California. (1993) 141–151
Duran, B.S. and Odell, P.L.: Cluster Analysis: A Survey. Springer-Verlag, Berlin (1974)
Cover, T.M. and Thomas J.A.: Elements of Information Theory. John Wiley & Sons, New York (1991)
Li, W.-H.: Molecular Evolution. Sinauer Associates, Sunderland MA (1997)
Collins, R.J., and Jefferson, D.R.: Selection in Massively Parallel Genetic Algorithms. In: Belew, R.K., and Booker L.B. (eds.): 4th ICGA. Morgan Kaufmann, San Mateo, California (1991) 249–256
Pielou, E.C.: Ecological Diversity. John Wiley & Sons, New York (1975)
Rosca, J.: Entropy-driven adaptive representation. In: J. Rosca (ed.): Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications. Tahoe City, California (1995) 23–32
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wineberg, M., Oppacher, F. (2003). The Underlying Similarity of Diversity Measures Used in Evolutionary Computation. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_21
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_21
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive