Amoebae for clustering: a bio-inspired cellular automata method for data classification

DOI10.1007/978-3-030-92551-2_23OpenAlexW4225963794MaRDI QIDQ2086744

Nazim Fatès, Emmanuel Jeandel, Amaury Saint-Jore

Publication date: 25 October 2022

Full work available at URL: https://doi.org/10.1007/978-3-030-92551-2_23

zbMATH Keywords

cellular automatadiscrete dynamical systemsdata clusteringbio-inspired modelscollective intelligence

Mathematics Subject Classification ID

Theory of computing (68Qxx)

Cites Work

• Probing robustness of cellular automata through variations of asynchronous updating
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• Self-organized societies: on the Sakoda model of social interactions
• The complexity of the asynchronous prediction of the majority automata
• Combinatorial game associated to the one dimensional Schelling's model of social segregation
• A portfolio of classification problems by one-dimensional cellular automata, over cyclic binary configurations and parallel update
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