An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Introduction to artificial life
Introduction to artificial life
Sequence complexity in Darwinian evolution
Complexity - Complex Adaptive systems: Part I
Causal architecture, complexity and self-organization in time series and cellular automata
Causal architecture, complexity and self-organization in time series and cellular automata
Digital ecosystems: evolving service-orientated architectures
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
An ecologically inspired simulation tool for managing digital ecosystems
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
A two-layer ecosystem evolution model: lessons from stages of mobile data services
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Managing a digital business ecosystem using a simulation tool
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Quality in software digital ecosystems the users perceptions
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Digital ecosystems: challenges and prospects
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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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. Self-organisation is perhaps one of the most desirable features in the systems that we engineer, and it is important for us to be able to measure self-organising behaviour. We investigate the self-organising aspects of Digital Ecosystems, created through the application of evolutionary computing to Multi-Agent Systems (MASs), aiming to determine a macroscopic variable to characterise the self-organisation of the evolving agent populations within. We study a measure for the self-organisation called Physical Complexity; based on statistical physics, automata theory, and information theory, providing a measure of information relative to the randomness in an organism's genome, by calculating the entropy in a population. We investigate an extension to include populations of variable length, and then built upon this to construct an efficiency measure to investigate clustering within evolving agent populations. Overall an insight has been achieved into where and how self-organisation occurs in our Digital Ecosystem, and how it can be quantified.