Abstract
Evolutionary processes such as convergent evolution and rapid adaptation suggest that there are constraints on how organisms evolve. Without constraint, such processes would most likely not be possible in the time frame in which they are seen. This paper investigates how artificial gene regulatory networks (GRNs), a connectionist architecture designed for computational problem solving may too be constrained in its evolutionary pathway. To understand this further, GRNs are applied to two different computational tasks and the way their underlying genes evolve over time is observed. From this, rules about how often genes are evolved and how this correlates with their connectivity within the GRN are deduced. By generating and applying these rules, we can build an understanding of how GRNs are constrained in their evolutionary path, and build measures to exploit this to improve evolutionary performance and speed.
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Turner, A.P., Lacey, G., Schoene, A., Dethlefs, N. (2019). Evolutionary Constraint in Artificial Gene Regulatory Networks. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_3
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DOI: https://doi.org/10.1007/978-3-319-97982-3_3
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