Computer Science – Neural and Evolutionary Computing
Scientific paper
2009-10-05
Computer Science
Neural and Evolutionary Computing
8 pages, 10 figures, ACM Management of Emergent Digital EcoSystems (MEDES) 2009
Scientific paper
A primary motivation for our research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. Here, we discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems, Service-Oriented Architectures, and distributed evolutionary computing. The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, considering the self-organised diversity of its evolving agent populations relative to the user request behaviour.
Briscoe Gerard
Wilde Philippe de
No associations
LandOfFree
Computing of Applied Digital Ecosystems does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Computing of Applied Digital Ecosystems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computing of Applied Digital Ecosystems will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-9298