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14 results

Artificial Immune System driven evolution in Swarm Chemistry.

Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2014)
Artificial Immune System driven evolution in Swarm Chemistry. In Proceedings of IEEE SASO 2014, (40-49). https://doi.org/10.1109/SASO.2014.16
Morphogenetic engineering represents an interesting field in which models, frameworks and algorithms can be tested in order to study how self-* properties and emergent behavio...

An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics.

Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2013)
An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics. In P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds.), Advances in Artifical Life, Proceedings of ECAL 2013, 864-871. https://doi.org/10.7551/978-0-262-31709-2-ch127
We describe an immune inspired approach to achieve self-expression within an ensemble, i.e. enabling an ensemble of autonomic components to dynamically change their coordinati...

Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks.

Conference Proceeding
Capodieci, N., Hart, E., & Cabri, G. (2013)
Designing self-aware adaptive systems: from autonomic computing to cognitive immune networks. In Proceedings of SASO Workshops 2013https://doi.org/10.1109/SASOW.2013.17
An autonomic system is composed of ensembles of heterogeneous autonomic components in which large sets of components are dynamically added and removed. Nodes within such an en...

Clustering Moving Data with a Modified Immune Algorithm

Conference Proceeding
Hart, E., & Ross, P. (2001)
Clustering Moving Data with a Modified Immune Algorithm. In E. Boers (Ed.), Applications of Evolutionary Computing, 394-403. https://doi.org/10.1007/3-540-45365-2_41
In this paper we present a prototype of a new model for performing clustering in large, non-static databases. Although many machine learning algorithms for data clustering hav...