- author = "Daniele Miorandi and Lidia Yamamoto and Francesco {De Pellegrini}",
- title = "A survey of evolutionary and embryogenic approaches to autonomic networking",
- journal = "Computer Networks",
- year = "2010",
- volume = "54",
- number = "6",
- pages = "944--959",
- month = "29 " # apr,
- ISSN = "1389-1286",
-
DOI = "
doi:10.1016/j.comnet.2009.08.021",
-
URL = "
http://www.sciencedirect.com/science/article/B6VRG-4X6FNS9-2/2/9be45a6ee371c9df9bae76c468300991",
- keywords = "genetic algorithms, genetic programming, Autonomic networking, Evolutionary computation, Genetic Algorithm, Chemical computing, Artificial embryogenies",
-
abstract = "The term 'autonomic networking' refers to
network-level software systems capable of
self-management, according to the principles outlined
by the Autonomic Computing initiative. Autonomicity is
widely recognized as a crucial property to harness the
growing complexity of current networked systems.
In this paper, we present a review of state-of-the-art techniques for the automated creation and evolution of software, with application to network-level functionalities. The main focus of the survey are biologically-inspired bottom-up approaches, in which complexity is grown from interactions among simpler units. First, we review evolutionary computation, highlighting aspects that apply to the automatic optimisation of computer programs in online, dynamic environments. Then, we review chemical computing, discussing its suitability as execution model for autonomic software undergoing self-optimization by code rewriting. Last, we survey approaches inspired by embryology, in which artificial entities undergo a developmental process. The overview is completed by an outlook into the major technical challenges for the application of the surveyed techniques to autonomic systems.",
