A visual demonstration of convergence properties of cooperative coevolution

We introduce a model for cooperative coevolutionary algorithms (CCEAs) using partial mixing, which allows us to compute the expected long-run convergence of such algorithms when individuals ’ fitness is based on the maximum payoff of some N evaluations with partners chosen at random from the other population. Using this model, we devise novel visualization mechanisms to attempt to qualitatively explain a difficult-to-conceptualize pathology in CCEAs: the tendency for them to converge to suboptimal Nash equilibria. We further demonstrate visually how increasing the size of N, or biasing the fitness to include an ideal-collaboration factor, both improve the likelihood of optimal convergence, and under which initial population configurations they are not much help

Eco-evolutionary dynamics on deformable fitness landscapes

Conventional approaches to modelling ecological dynamics often do not include evolutionary changes in the genetic makeup of component species and, conversely, conventional approaches to modelling evolutionary changes in the genetic makeup of a population often do not include ecological dynamics. But recently there has been considerable interest in understanding the interaction of evolutionary and ecological dynamics as coupled processes. However, in the context of complex multi-species ecosytems, especially where ecological and evolutionary timescales are similar, it is difficult to identify general organising principles that help us understand the structure and behaviour of complex ecosystems. Here we introduce a simple abstraction of coevolutionary interactions in a multi-species ecosystem. We model non-trophic ecological interactions based on a continuous but low-dimensional trait/niche space, where the location of each species in trait space affects the overlap of its resource utilisation with that of other species. The local depletion of available resources creates, in effect, a deformable fitness landscape that governs how the evolution of one species affects the selective pressures on other species. This enables us to study the coevolution of ecological interactions in an intuitive and easily visualisable manner. We observe that this model can exhibit either of the two behavioural modes discussed in the literature; namely, evolutionary stasis or Red Queen dynamics, i.e., continued evolutionary change. We find that which of these modes is observed depends on the lag or latency between the movement of a species in trait space and its effect on available resources. Specifically, if ecological change is nearly instantaneous compared to evolutionary change, stasis results; but conversely, if evolutionary timescales are closer to ecological timescales, such that resource depletion is not instantaneous on evolutionary timescales, then Red Queen dynamics result. We also observe that in the stasis mode, the overall utilisation of resources by the ecosystem is relatively efficient, with diverse species utilising different niches, whereas in the Red Queen mode the organisation of the ecosystem is such that species tend to clump together competing for overlapping resources. These models thereby suggest some basic conditions that influence the organisation of inter-species interactions and the balance of individual and collective adaptation in ecosystems, and likewise they also suggest factors that might be useful in engineering artificial coevolution

Retrospective analysis of varicella zoster virus (VZV) copy DNA numbers in plasma of immunocompetent patients with herpes zoster, of immunocompromised patients with disseminated VZV disease, and of asymptomatic solid organ transplant recipients

Background: Varicella zoster virus (VZV) causes significant morbidity and mortality in immunocompromised patients. Subclinical reactivation has been described in solid organ recipients and has been associated with graft versus host disease in bone marrow transplantation. Newer studies assessing the prevalence and impact of subclinical VZV reactivation in solid organ transplant (SOT) recipients are lacking. Methods and results: In a first step we developed a highly sensitive quantitative polymerase chain reaction (qPCR) assay for VZV DNA with a detection limit of < or = 20 copies/mL. Using this assay, we retrospectively analyzed plasma samples of different patient groups for VZV DNA. VZV DNA was found in 10/10 plasma samples of immunocompetent patients with herpes zoster (VZV copy numbers/mL: mean+/-SEM 1710+/-1018), in 1/1 sample of a human immunodeficiency virus-infected patient with primary VZV disease (15,192 copies/mL) and in 4/4 plasma samples of immunocompromised patients with visceral VZV disease (mean of first value 214,214+/-178,572). All 108 plasma samples of asymptomatic SOT recipients off any antiviral therapy, randomly sampled over 1 year, were negative for VZV DNA. Conclusion: Our qPCR assay proved to be highly sensitive (100%) in symptomatic VZV disease. We did not detect subclinical reactivation in asymptomatic SOT recipients during the first post-transplant year. Thus, subclinical VZV reactivation is either a rare event or does not exist. These data need to be confirmed in larger prospective trials

An Optimization Methodology for Memory Allocation and Task Scheduling in SoCs via Linear Programming

Abstract. Applications for system on chips become more and more complex. Also the number of available components (DSPs, ASICs, Memories, etc.) rises continuously. These facts necessitate a structured method for selecting components, mapping applications and evaluating the chosen configuration and mapping. In this work we present a methodology for the last named. We will consider optimization of memory allocation and task scheduling as a packing problem and minimize needed memory area. The results can be used as one element of an automated performance analysis for a given system on a high abstraction level. This analysis is essential for establishing a framework that iterates over a large quantity of possible systems. Considering a part of the H.264 codec as an example we will illustrate the results. Furthermore we will show that results can be retrieved fast compared to other NP-hard problems due to intelligent formulation of conditions within the linear program

A game-theoretic approach for designing mixed mutation strategies

Abstract. Different mutation operators have been proposed in evolutionary programming. However, each operator may be efficient in solving a subset of problems, but will fail in another one. Through a mixture of various mutation operators, it is possible to integrate their advantages together. This paper presents a game-theoretic approach for designing evolutionary programming with a mixed mutation strategy. The approach is applied to design a mixed strategy using Gaussian and Cauchy mutations. The experimental results show the mixed strategy can obtain the same performance as, or even better than the best of pure strategies.