Abstract
Bacteria have demonstrated an amazing capacity to overcome environmental changes by collective adaptation through genetic exchanges. Using a distributed communication system and sharing individual strategies, bacteria propagate mutations as innovations that allow them to survive in different environments. In this paper we present an agent-based model which is inspired by bacterial conjugation of DNA plasmids. In our approach, agents with bounded rationality interact in a common environment guided by local rules, leading to Complex Adaptive Systems that are named ’artificial societies’. We have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing heterogeneity levels and decentralizing communication structures, leading to a global adaptation of the system. This organic approach to model peer-to-peer dynamics in Complex Adaptive Systems is what we have named ‘bacterial-based algorithms’ because agents exchange strategic information in the same way that bacteria use conjugation and share genome.
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Gonzalez-Rodriguez, D., Hernandez-Carrion, J.R. (2014). A Bacterial-Based Algorithm to Simulate Complex Adaptive Systems. In: del Pobil, A.P., Chinellato, E., Martinez-Martin, E., Hallam, J., Cervera, E., Morales, A. (eds) From Animals to Animats 13. SAB 2014. Lecture Notes in Computer Science(), vol 8575. Springer, Cham. https://doi.org/10.1007/978-3-319-08864-8_24
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DOI: https://doi.org/10.1007/978-3-319-08864-8_24
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