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Digital ecosystems: Self-organisation of evolving agent populations

Briscoe, Gerard, De Wilde, Philippe (2009) Digital ecosystems: Self-organisation of evolving agent populations. In: MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems. . pp. 44-48. ACM ISBN 978-1-60558-829-2. (doi:10.1145/1643823.1643832) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:58025)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.1145/1643823.1643832

Abstract

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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/1643823.1643832
Uncontrolled keywords: Biological ecosystem; Clustering; Digital ecosystem; Dynamic problem; Efficiency measure; Evolutionary computing; Macroscopic variables; Measure of information; Physical complexity; Scalable architectures; Self-organisation; Self-organising; Statistical physics; Variable length, Automata theory; Computer crime; Entropy; Information theory; Multi agent systems; Population statistics, Ecosystems
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Philippe De Wilde
Date Deposited: 03 Jan 2023 16:15 UTC
Last Modified: 04 Jan 2023 14:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58025 (The current URI for this page, for reference purposes)

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