Name
Abstract | ||
|---|---|---|
In this paper we analyse the resilience of a peer-to-peer (P2P) evolutionary algorithm (EA) subject to the following dynamics: computing nodes acting as peers leave the system independently from each other causing a collective effect known as churn. Since the P2P EA has been designed to tackle large instances of computationally expensive problems, we will assess its behaviour under these conditions, by performing a scalability analysis in five different scenarios using the massively multimodal deceptive problem as a benchmark. In all cases, the P2P EA reaches the success criterion without a penalty on the runtime. We show that the key to the algorithm resilience is to ensure enough peers at the beginning of the experiment; even if some of them leave, those that remain contain enough information to guarantee a reliable convergence. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1504/IJHPSA.2008.024210 | IJHPSA |
Keywords | Field | DocType |
algorithm resilience,computationally expensive problem,enough information,peer-to-peer evolutionary algorithm,evolutionary algorithm,different scenario,large instance,collective effect,p2p ea,following dynamic,enough peer,scalability,evolutionary algorithms | Convergence (routing),Psychological resilience,Evolutionary algorithm,Peer-to-peer,Computer science,Peer to peer computing,Evolutionary computation,Scalability,Distributed computing | Journal |
Volume | Issue | Citations |
1 | 4 | 21 |
PageRank | References | Authors |
0.88 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. L. Laredo | 1 | 69 | 5.89 |
| P. A. Castillo | 2 | 134 | 13.95 |
| A. M. Mora | 3 | 99 | 10.00 |
| J. J. Merelo | 4 | 363 | 33.51 |
| C. Fernandes | 5 | 21 | 0.88 |