- author = "Michela Lorandi and Leonardo Lucio {Custode} and Giovanni Iacca",
- title = "Genetic Improvement of Routing Protocols for Delay Tolerant Networks",
- journal = "ACM Transactions on Evolutionary Learning and Optimization",
- year = "2021",
- volume = "1",
- number = "1",
- articleno = "04",
- month = may,
- keywords = "genetic algorithms, genetic programming, genetic improvement, PRoPHET, Ad hoc network, delay tolerant networks, epidemic routing",
- publisher = "Association for Computing Machinery",
- address = "New York, NY, USA",
- ISSN = "2688-299X",
-
URL = "
http://www.human-competitive.org/sites/default/files/humies-entry-iacca_0.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/telo-2020-38.r1_proof_fl.pdf",
-
URL = "https://doi.org/10.1145/3453683",
-
DOI = "
doi:10.1145/3453683",
- size = "37 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, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols 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. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalisability of the results across test cases.",
-
notes = "Entered 2021
HUMIES
https://dlnext.acm.org/journal/telo",
