Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing everdiversifying families of 'black box' optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a 'fitness function' specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure efficiency, so that (for example) the same candidates are not repeatedly visited; (iii) the absence of structural bias, which, if present, would predispose the algorithm towards limiting its search to specific regions of the solution space. The first two of these properties have been extensively investigated, however the third is little understood and rarely explored. In this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias. In particular, we state and prove a theorem concerning the dynamics of population variance in the case of real-valued search spaces and a 'flat' fitness landscape. This reveals how structural bias can arise and manifest as non-uniform clustering of the population over time. Critically, theory predicts that structural bias is exacerbated with (independently) increasing population size, and increasing problem difficulty. These predictions, supported by our empirical analyses, reveal two previously unrecognised aspects of structural bias that would seem vital for algorithm designers and practitioners. Respectively, (i) increasing the population size, though ostensibly promoting diversity, will magnify any inherent structural bias, and (ii) the effects of structural bias are more apparent when faced with (many classes of) 'difficult' problems. Our theoretical result also

We view Digital Ecosystems to be the digital counterparts of biological ecosystems, exploiting the self-organising properties of biological ecosystems, which are considered to be robust, self-organising and scalable architectures that can automatically solve complex, dynamic problems. Digital Ecosystems are a novel optimisation technique where the optimisation works at two levels: a first optimisation, migration of agents (representing services) which are distributed in a decentralised peer-topeer 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 created an Ecosystem-Oriented Architecture (EOA) of Digital Ecosystems by extending Service-Oriented Architectures (SOAs) with distributed evolutionary computing (DEC), allowing services to recombine and evolve over time, constantly seeking to improve their effectiveness for the user base. Individuals within our Digital Ecosystem will be applications (groups of services), created in response to user requests by using evolutionary optimisation to aggregate the services. These individuals will migrate through the Digital Ecosystem and adapt to find niches where they are useful in fulfilling other user requests for applications. Simulation results imply that the Digital Ecosystem performs better at large scales than a comparable SOA, suggesting that incorporating ideas from theoretical ecology can contribute to useful self-organising properties in digital ecosystems.

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