Abstract
Biologists are interested in studying the relation between the genetic diversity of a population and its fitness. We adopt the notion of entropy as a measure of genetic diversity and correlate it with fitness of an evolutionary ecosystem simulation. EcoSim is a predator-prey individual based simulation which models co-evolving sexual individuals evolving in a dynamic environment. The correlation values between entropy and fitness of all the species that ever existed during the whole simulation are presented. We show how entropy strongly correlates with fitness and investigate the factors behind this result using machine learning techniques. We build a classifier based on different species’ features and successfully predict the resulting correlation value between entropy and fitness. The best features affecting the quality of classification are also being investigated.
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References
Aspinal, A., Gras, R.: K-Means Clustering as a Speciation Mechanism within an Individual-Based Evolving Predator-Prey Ecosystem Simulation. In: An, A., Lingras, P., Petty, S., Huang, R. (eds.) AMT 2010. LNCS, vol. 6335, pp. 318–329. Springer, Heidelberg (2010)
Bergstrom, C., Lachmann, M.: Shannon information and biological fitness. In: IEEE Information Theory Workshop, pp. 50–54 (2004)
EcoSim: An Ecosystem Simulation, http://sites.google.com/site/ecosimgroup/research/ecosystem-simulation
Gras, R., Devaurs, D., Wozniak, A., Aspinall, A.: An individual-based evolving predator-prey ecosystem simulation using fuzzy cognitive map as behavior model. Artificial Life 15(4), 423–463 (2009)
Gras, R., Golestani, A., Hosseini, M., Khater, M., Farahani, Y.M., Mashayekhi, M., Ibne, S.M., Sajadi, A., Salehi, E., Scott, R.: Ecosim: an individual-based platform for studying evolution. In: European Conference on Artificial Life (in press, 2011)
Kosko, B.: Fuzzy cognitive maps. Int. Journal of Man-Machine Studies, 65–75 (1986)
Markert, J., Champlin, D., Gutjahr-Gobell, R., Grear, J., Kuhn, A., McGreevy, T., Roth, A., Bagley, M., Nacci, D.: Population genetic diversity and fitness in multiple environments. BMC Evolutionary Biology 10, 1471–2148–10–205 (2010)
Oostermeijer, J., van Eijck, M., den Nijs, J.: Offspring fitness in relation to population size and genetic variation in the rare perennial plant species gentiana pneumonanthe (gentianceae). Oecologia 97, 289–296 (1994)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)
Reed, D., Frankham, R.: Correlation between fitness and genetic diversity. Conserv. Biology 17, 230–237 (2003)
Saeys, Y., Inza, I., Larraaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Salehi, E., Gras, R.: Efficient eda for large optimization problem via constraining the search space of models. In: Genetic and Evolutionary Computation Conference, Dublin, Ireland (in press, 2011)
Shannon, C.: A mathematical theory of communication. Bell Systems Technical Journal, 379–423 (1948)
Sherwin, W.B.: Entropy and information approaches to genetic diversity and its expression: Genomic geography. Entropy 12, 1765–1798 (2010)
Siegel, S.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill, New York (1956)
Vandewoestijne, S., Schtickzelle, N., Baguette, M.: Positive correlation between genetic diversity and fitness in a large, well-connected metapopulation. BMC Biology 6, 1741–7007–6–46 (2008)
Witten, I., Frank, E.: Data Mining- Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, USA (2000)
Yang, Q., Salehi, E., Gras, R.: Using Feature Selection Approaches to Find the Dependent Features. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6113, pp. 487–494. Springer, Heidelberg (2010)
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Khater, M., Salehi, E., Gras, R. (2011). Correlation between Genetic Diversity and Fitness in a Predator-Prey Ecosystem Simulation. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_43
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DOI: https://doi.org/10.1007/978-3-642-25832-9_43
Publisher Name: Springer, Berlin, Heidelberg
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