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. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss 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, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the ecological succession (development).

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle.

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Premature convergence is a persisting problem in evolutionary optimisation, in particular -genetic algorithms. While a number of methods exist to approach this issue, they usually require problem specific calibration or only partially resolve the issue, at best by delaying the premature convergence of an evolving population. Analytical models in biology show that resiliently diverse populations evolve on high-dimensional fitness landscapes with "holey" rather than "rugged" topographies, but the implications for artificial evolutionary systems remain largely unexplored. Here I show how holey fitness landscapes (HFLs) can be incorporated in an evolutionary algorithm and use this approach to investigate the ability of HFLs to maintain genetic diversity in an evolving population. The results indicate that an underlying HFL can counteract premature genetic convergence and sustain diversity. They also suggest that HFL may provide a flexible mechanism for dynamic creation and maintenance of subpopulations that concentrate their evolutionary search in different regions of the solution space. Finally, I discuss on-going work on using the HFL model in optimisation problems.

A genetic algorithm (GA) is utilised to discover known and novel PROSITE-like sequence templates that can be used to classify the sub-cellular location of eukaryotic proteins. While traditional machine learning techniques present a black-box approach to this problem, the current method explicitly represents the discovered localisation motifs. A combined multi-class location classifier is presented and compared to other techniques based on genetic programming. Without consideration of additional structural information the presented method outperforms the alternative techniques.