Szerző dc.contributor.author | Jobbágy Zoltán | |
Elérhetőség dátuma dc.date.accessioned | 2019-12-06T12:06:53Z | |
Rendelkezésre állás dátuma dc.date.available | 2019-12-06T12:06:53Z | |
Kiadás dc.date.issued | 2016 | |
Issn dc.identifier.issn | 2498-5392 | |
Uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/14534 | |
Kivonat dc.description.abstract | Military operations are very complex undertakings. However, complexity is not a feature unique to military operations. When biologists wanted to understand the properties of gene mutation they also faced complexity. Confronted by a large number of genes featuring different characteristics, a difficult-to-decode interac- tion among those genes, and an environment that could not be excluded as a factor, Sewell Wright introduced the shifting balance theory, also known as the theory of the fitness landscape. The theory allows complexity to be seen as a process that rests on adaptation and mutation. These two processes are also central to military operations as it is imperative to offset the changing conditions coming both from the environment and the interaction with the enemy. In the article the author uses Wright’s theory to help see military operations as a complex optimization problem that includes approximations and estimations regarding optimal values. | en |
Nyelv dc.language.iso | en | en |
Kulcsszó dc.subject | network-centric warfare | en |
Kulcsszó dc.subject | complex optimization | en |
Kulcsszó dc.subject | biological evolution | en |
Kulcsszó dc.subject | fitness landscape | en |
Kulcsszó dc.subject | adaptation | en |
Cím dc.title | Network Centric Warfare as Complex Optimization: An Evolutionary Approach | en |
Típus dc.type | Folyóiratcikk | hu_HU |
Változat dc.description.version | kiadói | hu_HU |
Doi azonosító dc.identifier.doi | 10.32565/aarms.2016.2.1 | |
Folyóirat dc.identifier.journalTitle | AARMS | hu_HU |
Évfolyam dc.identifier.journalVolume | 15 | hu_HU |
dc.identifier.page | 93-105 | hu_HU |
Füzetszám dc.identifier.journalIssueNumber | 2 | hu_HU |