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

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

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

Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments

The genetic architecture underlying the evolution of a rare piscivorous life history form in brown trout after secondary contact and strong introgression

Identifying the genetic basis underlying phenotypic divergence and reproductive isolation is a longstanding problem in evolutionary biology. Genetic signals of adaptation and reproductive isolation are often confounded by a wide range of factors, such as variation in demographic history or genomic features. Brown trout ( ) in the Loch Maree catchment, Scotland, exhibit reproductively isolated divergent life history morphs, including a rare piscivorous (ferox) life history form displaying larger body size, greater longevity and delayed maturation compared to sympatric benthivorous brown trout. Using a dataset of 16,066 SNPs, we analyzed the evolutionary history and genetic architecture underlying this divergence. We found that ferox trout and benthivorous brown trout most likely evolved after recent secondary contact of two distinct glacial lineages, and identified 33 genomic outlier windows across the genome, of which several have most likely formed through selection. We further identified twelve candidate genes and biological pathways related to growth, development and immune response potentially underpinning the observed phenotypic differences. The identification of clear genomic signals divergent between life history phenotypes and potentially linked to reproductive isolation, through size assortative mating, as well as the identification of the underlying demographic history, highlights the power of genomic studies of young species pairs for understanding the factors shaping genetic differentiation

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

The History and geography of the Y chromosome SNPs in Europe: an update

The knowledge of the evolution of the human genome is strictly dependent on the availability of appropriate genetic markers and their relative coverage of genetic variation which refine the phylogenetic reconstruction. While autosomal markers are particularly valuable for recognizing correspondence between genetic and geographic distances, markers on mitochondrial DNA (mtDNA) or Non Recombining Portion of Y Chromosome (NRY), because of their unilinear transmission, can effectively trace diachronical patterns of the human peopling. The maximum extent of polymorphism coverage has already been reached for the very small mitochondrial genome (about 16,5 Kbp), whereas the first studies based on RFLPs (Restriction Fragment Length Polymorphisms) (Cann et al., 1987) and on sequencing of the hypervariable regions (Vigliant et al., 1991), were then combined to get higher resolution (Torroni et al., 1996), and finally the complete genome sequencing is now routinely performed (Achilli et al., 2004, Pala et al., 2009), in order to detect the whole mtDNA variation. A similar approach cannot be used yet at population level for the by far larger nuclear genome. However, advances in genotyping technology have dramatically enhanced the resolution of the analysis at genome-wide level, and recent papers significantly improved the knowledge of the relationships among European populations, using 300 to 500 K SNPs (Single Nucleotide Polymorphisms) on microarrays chips (Tian et al., 2008; Novembre et al., 2008). As to the NRY, most of the studies before the year 2000 were performed using Alu insertion (Hammer, 1995) or STRs (Short Tandem Repeats) (De Knijff et al., 1997; Pritchard et al., 1999) with the known limitations due to recurrence and reversion of this kind of polymorphisms. Using D-HPLC (Denaturing High Performance Liquid Chromatography) technology, Underhill and coworkers (1997) discovered 22 new SNP biallelic markers, rapidly raising in number to 167 (Underhill et al., 2000), 242 (YCC, 2002), about 600 (Karafet et al., 2008), up to more than 725 presently listed in the Y-DNA SNP Index 2009, (www.isogg. org), and the knowledge of Y chromosome phylogeny and of the spread worldwide of human populations raised proportionally. The next goal of the research on Y chromosome will be the use of specific microarrays that can genotype a much higher number of SNPs than nowadays routinely performed, and, ultimately, the complete Y chromosome sequencing. Waiting for future developments, this short note reports the state of the art of the phylogenetic (“history”) and phylogeographic (“geography”) research on Y chromosome SNP analyses in Europe, updating the review published in this Journal by Francalacci & Sanna at the beginning of 2008

Regional Genetic Structure in the Aquatic Macrophyte Ruppia cirrhosa Suggests Dispersal by Waterbirds

The evolutionary history of the genus Ruppia has been shaped by hybridization, polyploidisation and vicariance that have resulted in a problematic taxonomy. Recent studies provided insight into species circumscription, organelle takeover by hybridization, and revealed the importance of verifying species identification to avoid distorting effects of mixing different species, when estimating population connectivity. In the present study, we use microsatellite markers to determine population diversity and connectivity patterns in Ruppia cirrhosa including two spatial scales: (1) from the Atlantic Iberian coastline in Portugal to the Siculo-Tunisian Strait in Sicily and (2) within the Iberian Peninsula comprising the Atlantic-Mediterranean transition. The higher diversity in the Mediterranean Sea suggests that populations have had longer persistence there, suggesting a possible origin and/or refugial area for the species. The high genotypic diversities highlight the importance of sexual reproduction for survival and maintenance of populations. Results revealed a regional population structure matching a continent-island model, with strong genetic isolation and low gene flow between populations. This population structure could be maintained by waterbirds, acting as occasional dispersal vectors. This information elucidates ecological strategies of brackish plant species in coastal lagoons, suggesting mechanisms used by this species to colonize new isolated habitats and dominate brackish aquatic macrophyte systems, yet maintaining strong genetic structure suggestive of very low dispersal.Fundacao para a Cincia e Tecnologia (FCT, Portugal) [PTDC/MAR/119363/2010, BIODIVERSA/0004/2015, UID/Multi/04326/2013]Pew FoundationSENECA FoundationMurcia Government, Spain [11881/PI/09]FCT Investigator Programme-Career Development [IF/00998/2014]Spanish Ministry of Education [AP2008-01209]European Community [00399/2012]info:eu-repo/semantics/publishedVersio