On Adaptive Specialisation in Genetic Improvement

Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve it’s efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history

Warp-speed adaptation to novel hosts after 300 generations of enforced dietary specialisation in the seed beetle Callosobruchus maculatus (Coleoptera: Chrysomelidae: Bruchinae)

Thank you to Paul Eady for providing C. maculatus to initiate our laboratory population and advice on rearing them. The study was funded by the University of Aberdeen core teaching funds (honours project budget to TP), and by a doctoral training grant to AL from the BBSRC-EastBio doctoral training partnershipPeer reviewedPublisher PD

Adaptation to climate in widespread eucalypt species

AbstractThe long term success of revegetation efforts will depend upon the planted species’ resilience to climate change. Many widespread species grow across a range of climatic conditions and, thus, may possess adaptations that could be utilised to improve climate resilience of restored ecosystems. Species can achieve a widespread distribution via two main mechanisms; (1) by diverging into a series of specialised populations, or (2) through high phenotypic plasticity. The extent to which populations are specialised or plastic in response to climate will determine the seed-sourcing strategy required for optimal restoration outcomes under a changing climate. We examined genetic divergence and phenotypic plasticity in two widespread Eucalyptus species (E. tricarpa in southeastern Australia, E. salubris in southwestern Australia), to determine the nature of adaptation to climate in these species, and whether genomic screening might be a useful tool to assess climate adaptation.We examined nine populations of each species across climate gradients and, for E. tricarpa, trees originating from the same populations were also studied in two common garden field trials. We characterised responses in functional traits relevant to climate adaptation, including leaf size, thickness, tissue density, and carbon isotope ratio (δ13C). Genetic variation was assessed with genome scans using DArTseq markers, and ‘outlier markers’ were identified as being linked to regions of the genome that are potentially under selection.Evidence of both plastic response and genetic specialisation for climate was found in both species, indicating that widespread eucalypts utilise a combination of both mechanisms for adaptation to spatial variation in climate. The E. tricarpa common garden data suggested high plasticity in most of the measured functional traits, and the extent of plasticity in some traits (e.g. leaf size and thickness) varied among provenances, suggesting genetic variation for plasticity itself. In E. salubris, most functional traits showed little variation across the gradient. However, water use efficiency appeared highly plastic, as determined from the strong correlation between δ13C and recent precipitation (R2 = 0.83). Both species showed spatial partitioning of genetic variation across the gradient, and data for E. salubris revealed two distinct lineages. The genome scans yielded 16,122 DArTseq markers for “Lineage 1” of E. salubris, of which 0.1% were potentially adaptive ‘outlier loci’, and 6,544 markers for E. tricarpa, of which 2.6% were outliers. Canonical Analysis of Principal Coordinates (CAP) analysis showed that the outlier markers were correlated with climatic variables, and some were also strongly correlated with functional traits. An ‘Aridity Index’ was also developed from the CAP analysis that has potential as a tool for environmental planners to use for matching seed sources to target climates.Widespread eucalypts are likely to possess a capacity to respond plastically to a changing climate to some extent, but selection of seed sources to match projected climate changes may confer even greater climate resilience. Further study of the mechanisms of plasticity in response to climate may improve our ability to assess climate adaptation in other species, and to determine optimal strategies for ecosystem restoration and management under climate change

Digital Ecosystems: Ecosystem-Oriented Architectures

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

Biology of Applied Digital Ecosystems

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).Comment: 9 pages, 4 figure, conferenc

Detail-oriented cognitive style and social communicative deficits, within and beyond the autism spectrum: independent traits that grow into developmental interdependence

At the heart of debates over underlying causes of autism is the "Kanner hypothesis" that autistic deficits in social reciprocity, and a cognitive/perceptual 'style' favouring detail-oriented cognition, co-vary in autistic individuals. A separate line of work indicates these two domains are normally distributed throughout the population, with autism representing an extremity. This realisation brings the Kanner debate into the realm of normative co-variation, providing more ways to test the hypothesis, and insights into typical development; for instance, in the context of normative functioning, the Kanner hypothesis implies social costs to spatial/numerical prowess

Evolving Neural Networks for the Capture Game

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Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE