CINECA IRIS Institutional Research Information System
IRIS è il sistema di gestione integrata dei dati della ricerca (persone, progetti, pubblicazioni, attività) adottato dall'Università degli Studi di Torino.
AperTO è l'archivio istituzionale Open Access destinato a raccogliere, rendere visibile e conservare la produzione scientifica dell'Università degli Studi di Torino.
EnglishIn this work we demonstrate in two different contexts how we can introduce recent discoveries and technological advances into existing computational models. In the first case, we worked on improving the performance of a simple paradigm for distributed computation: cellular automata. This was achieved by applying principles inspired by Darwinian evolution to alter the connections between the cells of the system, hence changing its topological structure. We have studied the performance of these evolved structures on prototypical problems, and analyzed their response to probabilistic transient faults and permanent failures. In the second case, we consider the context of biological genetic regulatory networks and in particular a model thereof proposed by Kaufmann in the late 60’s: random Boolean networks. Since the model was developed, biology has made tremendous progress and these new discoveries can be used to improve the original model. From the structure of the network, to timing of the event taking place on it, to the specifics of the genes’ activation, we have added a great deal of modern knowledge into the original model, studying , analyzing, and validating it on biological case studies.
| Titolo: | Toward robust network based complex systems: from evolutionary cellular automata to biological models | |
| Autori Riconosciuti: | ||
| Autori: | Ch. Darabos; M. Tomassini; F. Di Cunto; P. Provero; J.H. Moore; Mario Giacobini | |
| Data di pubblicazione: | 2011 | |
| Abstract: | In this work we demonstrate in two different contexts how we can introduce recent discoveries and technological advances into existing computational models. In the first case, we worked on improving the performance of a simple paradigm for distributed computation: cellular automata. This was achieved by applying principles inspired by Darwinian evolution to alter the connections between the cells of the system, hence changing its topological structure. We have studied the performance of these evolved structures on prototypical problems, and analyzed their response to probabilistic transient faults and permanent failures. In the second case, we consider the context of biological genetic regulatory networks and in particular a model thereof proposed by Kaufmann in the late 60’s: random Boolean networks. Since the model was developed, biology has made tremendous progress and these new discoveries can be used to improve the original model. From the structure of the network, to timing of the event taking place on it, to the specifics of the genes’ activation, we have added a great deal of modern knowledge into the original model, studying , analyzing, and validating it on biological case studies. | |
| Volume: | 5 | |
| Pagina iniziale: | 37 | |
| Pagina finale: | 47 | |
| Digital Object Identifier (DOI): | 10.3233/IA-2011-0003 | |
| Parole Chiave: | complex systems; networks; evolutionary algorithm; cellular automata; small-worlds; biological regulatory networks; scale-free networks | |
| Rivista: | INTELLIGENZA ARTIFICIALE | |
| Appare nelle tipologie: | 03A-Articolo su Rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 554407_oa.pdf | PDF EDITORIALE | Accesso riservato | Utenti riconosciuti Richiedi una copia |
http://hdl.handle.net/2318/84834
Copyright © 2022