  We view Digital Ecosystems to be the digital counterparts of biological
ecosystems, which are considered to be robust, self-organising and scalable
architectures that can automatically solve complex, dynamic problems. So, this
work is concerned with the creation, investigation, and optimisation of Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems.
First, we created the Digital Ecosystem, a novel optimisation technique
inspired by biological ecosystems, where the optimisation works at two levels:
a first optimisation, migration of agents which are distributed in a
decentralised peer-to-peer network, operating continuously in time; this
process feeds a second optimisation based on evolutionary computing that
operates locally on single peers and is aimed at finding solutions to satisfy
locally relevant constraints. We then investigated its self-organising aspects,
starting with an extension to the definition of Physical Complexity to include
evolving agent populations. Next, we established stability of evolving agent
populations over time, by extending the Chli-DeWilde definition of agent
stability to include evolutionary dynamics. Further, we evaluated the diversity
of the software agents within evolving agent populations. To conclude, we
considered alternative augmentations to optimise and accelerate our Digital
Ecosystem, by studying the accelerating effect of a clustering catalyst on the
evolutionary dynamics. We also studied the optimising effect of targeted
migration on the ecological dynamics, through the indirect and emergent
optimisation of the agent migration patterns. Overall, we have advanced the
understanding of creating Digital Ecosystems, the self-organisation that occurs
within them, and the optimisation of their Ecosystem-Oriented Architecture.

Multiagent Systems;Neural and Evolutionary Computing