Dual phase evolution — a mechanism for self-organization in complex systems

Green, David G.; Leishman, Tania Gaye; Sadedin, Suzanne

doi:10.1007/978-3-540-85081-6_8

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…When the landscape is fragmented, populations may diverge, solving the same problems in different ways. It has been suggested that the evolution of complexity in nature involves repeated landscape phase changes, allowing selection to alternate between local and global search [32]. So, we can potentially take advantage of this by using a diverse heterogeneous distributed landscape (topology), i.e.…”

Section: A Network and Spatial Dynamicsmentioning

confidence: 99%

Ecosystem-oriented distributed evolutionary computing

Briscoe

Wilde

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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Abstract-We create a novel optimisation technique inspired by natural ecosystems, where the optimisation works at two levels: a first optimisation, migration of genes which are distributed in a 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 consider from the domain of computer science distributed evolutionary computing, with the relevant theory from the domain of theoretical biology, including the fields of evolutionary and ecological theory, the topological structure of ecosystems, and evolutionary processes within distributed environments. We then define ecosystemoriented distributed evolutionary computing, imbibed with the properties of self-organisation, scalability and sustainability from natural ecosystems, including a novel form of distributed evolutionary computing. Finally, we conclude with a discussion of the apparent compromises resulting from the hybrid model created, such as the network topology.

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Section: A Network and Spatial Dynamicsmentioning

confidence: 99%

Ecosystem-oriented distributed evolutionary computing

Briscoe

Wilde

2012

2012 12th UK Workshop on Computational Intelligence (UKCI)

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“…When the landscape is fragmented, populations may diverge, solving the same problems in different ways. Recently, it has been suggested that the evolution of complexity in nature involves repeated landscape phase changes, allowing selection to alternate between local and global search [65].…”

Section: Network and Spatial Dynamicsmentioning

confidence: 99%

Digital Ecosystems: Ecosystem-Oriented Architectures

2011

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We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, 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. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

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“…Previous research has shown that CAS can be described in terms of networks of interacting components (Green, 1993) and that structural properties of these underlying networks may be used to explain many of the processes observed in CAS. Based on this realisation, the notion of Dual Phase Evolution (DPE) was proposed (Green et al, 2006;Green et al, 2000). In short, DPE explains CAS properties such as perpetual novelty and diversity, modularity, and complexity on all scales in terms of recurring phase transitions in connectivity and interaction patterns of underlying networks.…”

Section: Introductionmentioning

confidence: 99%

The Dual Phase Evolution Framework for Understanding Evolutionary Dynamics in Complex Adaptive Systems

Paperin

Sadedin²

2009

Proceedings of the International Joint Conference on Computational Intelligence

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Evidence from several fields suggests that dual phase evolution (DPE) may account for distinctive features associated with complex adaptive systems. Here, we review empirical and theoretical evidence for DPE in natural systems and examine the relationship of DPE to self-organized criticality and adaptive cycles. A general model for DPE in networks is outlined, with preliminary data illustrating the emergence of phase changes. DUAL PHASE EVOLUTION ExamplesEvidence from several fields suggests that phase changes in landscape connectivity form a powerful agent of evolutionary change and innovation. Disasters often mediate long-term changes in the composition of ecological communities, with 135

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Dual phase evolution — a mechanism for self-organization in complex systems

Cited by 5 publications

References 13 publications

Ecosystem-oriented distributed evolutionary computing

Ecosystem-oriented distributed evolutionary computing

Digital Ecosystems: Ecosystem-Oriented Architectures

The Dual Phase Evolution Framework for Understanding Evolutionary Dynamics in Complex Adaptive Systems

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