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Adaptation and Genomic Evolution in EcoSim

  • Conference paper
From Animals to Animats 12 (SAB 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7426))

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Abstract

Artificial life evolutionary systems facilitate addressing lots of fundamental questions in evolutionary genetics. Behavioral adaptation requires long term evolution with continuous emergence of new traits, governed by natural selection. We model organism’s genomes coding for their behavioral model and represented by fuzzy cognitive maps (FCM), in an individual-based evolutionary ecosystem simulation (EcoSim). Our system allows the emergence of new traits and disappearing of others, throughout a course of evolution. In this paper we show how continuous adaptation to a changing environment affects genomic structure and genetic diversity. We adopted the notion of Shannon entropy as a measure of genetic diversity. We emphasized the difference in genetic diversity between EcoSim and its neutral model (a partially randomized version of EcoSim). In addition, we studied the effect that genetic diversity has on species fitness and we showed how they correlate with each other. We used Random Forest to build a classifier to further validate our findings, along with some meaningful rule extraction.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Windsor, ON, Canada

    Marwa Khater & Robin Gras

Authors
  1. Marwa Khater
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  2. Robin Gras
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Editor information

Editors and Affiliations

  1. Informatics Research Centre, University of Skövde, Kanikegränd 3A, 54134, Skövde, Sweden

    Tom Ziemke

  2. Lund University, Lundagård, Kungshuset, 22222, Lund, Sweden

    Christian Balkenius

  3. Mærsk McKinney Møller Institute, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark

    John Hallam

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Khater, M., Gras, R. (2012). Adaptation and Genomic Evolution in EcoSim. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_22

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  • DOI: https://doi.org/10.1007/978-3-642-33093-3_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33092-6

  • Online ISBN: 978-3-642-33093-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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