- author = "Michela Lorandi and Leonardo Lucio {Custode} and Giovanni Iacca",
- title = "Genetic Improvement of Routing Protocols for Delay Tolerant Networks",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- series = "GECCO '21",
- year = "2021",
- editor = "Carola Doerr",
- month = "10-14 " # jul,
- pages = "35--36",
- organisation = "SIGEVO",
- address = "Internet",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, genetic improvement, PRoPHET, Ad hoc network, delay tolerant networks, epidemic routing",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3462716",
- size = "2 pages",
- abstract = "Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic store and forward replication-based approaches, which Improving Assertion Oracles with Evolutionary however produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.",
-
notes = "Short version of
\cite{Lorandi:2021:TELO}
https://dlnext.acm.org/journal/telo",
