Adaptive Dynamics

The topic of evolution in interacting species was interpreted to focus upon predator-prey coevolution. This would enable a sound theoretical basis to be constructed for the analysis of evolution in larger systems, such as food webs. As a result all the systems explored in the project were predator-prey systems.
The use of analytical, numerical, and simulation models to study predator­ prey coevolution has shown that there occur a variety of consequences for the ecological dynamics of evolving predator-prey systems.  Evolutionary change can result in a wide range of dynamical behaviour, from stable equilibria to chaos.  The consequences of natural selection need not be stability, as some single-species models and experiments have suggested.  However, ecological dynamic stability is not required for evolutionary persistence.
The results obtained support arguments for the misleading nature of local asymptotic stability as a measure of the dynamics of ecological systems. However, it was not found possible to use permanence as an alternative to local asymptotic stability, as had been the original intention.
Results from the simulation models showed that convergence to ecological equilibrium was not necessarily to be expected in systems of evolving interacting species, in contrast to many models of community assembly and evolution.  The size of the potential parameter space of the simulation models meant that not all the dynamical behaviour of these models could be explored in the time scale of the project.
The analytical models I developed enabled an exploration of evolutionary and ecological dynamics more thoroughly than the simulation models, and compensated for the limited understanding of the simulation systems.  A wide range of evolutionary dynamics, such as ESSs and Red Queen coevolution, were seen to be produced from very simple systems, implying that more complex systems, such as food webs, would have much more interesting evolutionary consequences.  The use of "phenotype space" based techniques enabled a more global understanding of the evolutionary and ecological dynamics of predator-prey interactions than had previously been possible.
In general, the results supported the contentions that both ecological and evolutionary dynamics need to be taken into consideration when studying the evolution of interacting species; and that ecological interactions need to be considered in evolutionary models. The techniques I used to address these questions have potential applications in future research.

The persistence of environmental problems in urban areas and the prospect of increasing congestion have precipitated a variety of new policies in the USA, with concomitant analytical and modeling requirements for transportation planning. This paper introduces the Sequenced Activity-Mobility Simulator (SAMS), a dynamic and integrated microsimulation forecasting system for transportation, land use and air quality, designed to overcome the deficiencies of conventional four-step travel demand forecasting systems. The proposed SAMS framework represents a departure from many of the conventional paradigms in travel demand forecasting. In particular, it aims at replicating the adaptative dynamics underlying transportation phenomena; explicitly incorporates the time-of-day dimension; represents human behavior based on the satisficing, as opposed to optimizing, principle; and endogenously forecasts socio-demographic, land use, vehicle fleet mix, and other variables that have traditionally been projected externally to be input into the forecasting process.

It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system.

Evolutionary linguistics is going to clarify the evolutionary process of language and evolutionary economics is clarifying the dynamics of economic phenomena. In this paper, we consider the relevance between these two fields. In evolutionary linguistics, the origin of language is thought of as the biological evolutionary process of language capacity of humans and the evolution of language the processes of complexification and structuralization of language structure. The evolutionary process of language forms a double loop dynamics among biological evolution, learning and cultural evolution. This double loop among these three adaptive dynamics ranging in large spatio-temporal scales characterizes language evolution as complex systems. The double loop dynamics can also be found in the origin and the evolution of economics. Thus, evolutionary linguistics and evolutionary economics can collaborate in understanding of complex evolutionary phenomena. The constructive approach is a useful ...

Agent architectures need to organize themselves and adapt dynamically to changing circumstances without top-down control from a system operator. Some researchers provide this capability with complex agents that emulate human intelligence and reason explicitly about their ...

Abstract: Toward developing of comprehensive engineering (dynamic) evolutionary paradigma  via structural analysis of behavior motivation  in eco-evo sequences  following by history of humanity, on the basis of correlational changes in thermodynamics of evolutionary Semi-Open System of Biosphere+Anthroposphere in Antropocene, realistic but probabilistic, materialistic, with no esoteric mysticism, natural-science-based ones, that analyses  reasons of changes in the  interaction of the information and energy-matter-works goal-aimed dynamics    by gradual  structural analysis  for physical dynamical interactions among components of the ComplexAdaptive System of the Biosphere dynamics
20July19

This paper presents a functional design and modeling of smart conditioning circuit of a vibrational energy harvester based on electrostatic transducer. Two original features are added to the basic configuration previously published (whose model we presented on BMAS2007 conference). Firstly, we developed an auto-calibration block which allows the new harvester to adapt dynamically to the varying environment parameters (e.g., amplitude of external vibrations). Secondly, we propose an original schematic configuration based on dual output DC-DC converter, which implements a smart power interface with the load, allowing the harvester to manage a possibly variable load and adapt to different situations (e.g. unsufficient generated power level, load too large, etc.). The scheme of the power interface re-uses the coil existing in the basic harvester configuration. The new harvester architecture contains ldquosoftwarerdquo blocks which can be programmed to implement different power-management and auto-calibration strategies. We describe one possible algorithm of the whole architecture operation, and present the corresponding modeling results. The system is implemented as a mixed VHDL-AMS/ELDO model.

Species exist in complex biotic environments, engaging in a variety of antagonistic and cooperative interactions. While these interactions are generally recognized to be context-dependent, varying in outcome in the presence of other interactions, studies tend to focus on each interaction in isolation. One of the main classes of species interaction is mutualism, in which partner species gain a net benefit from their interaction. However, mutualisms are beset by a variety of species that can reduce or even eliminate the benefits of mutualism through exploitation of and competition for the resources and services offered by mutualists. These exploiter species potentially threaten the ecological stability of mutualisms and may alter selection on mutualistic traits. Thus, understanding the ecology and evolution of mutualisms requires consideration of interactions with exploiter species. In this dissertation, I investigated the effects of exploiter species on mutualisms between plants and pollinators using a combination of eco-evolutionary modeling, optimization theory, and behavioral studies. Using two adaptive dynamics models of coevolution in exploited pollinating seed parasite mutualisms, I found that exploiters reduce mutualist densities and select for more parasitic mutualists. Nevertheless, the models demonstrate that intraspecific competition for host resources and host defense of those resources restrict the ecological conditions that lead to extinction of the mutualism, as well as the chances of evolution to extinction. Thus, exploiters are unlikely to be the threat to mutualisms that has been assumed previously. On the other hand, in another type of exploitation, exploitative predators may pose a greater threat to investment in mutualism than has been presumed. Through both optimal foraging theory and behavioral experiments on bumble bees, I found that the risk from ambush predators can change pollinator floral preferences when predators preferentially use high-quality flowers to locate their prey. This research suggests that predators of mutualists may have important top-down effects and that further research is needed to investigate the effects of exploitative predators on selection on mutualist traits.

We present Lazy Binary Splitting (LBS), a user-level scheduler of nested parallelism for shared-memory multiprocessors that builds on existing Eager Binary Splitting work-stealing (EBS) implemented in Intel's Threading Building Blocks (TBB), but improves performance and ease-of-programming. In its simplest form (SP), EBS requires manual tuning by repeatedly running the application under carefully controlled conditions to determine a stop-splitting-threshold (sst) for every do-all loop in the code. This threshold ...

Purpose: Non-linear regression analysis was used to determine dark adaptation indices in people with retinitis pigmentosa and in control subjects.Methods: Dark adaptation data were collected for 13 people with retinitis pigmentosa and 21 controls using the Goldmann-Weekers Dark Adaptometer. Data were analysed using an exponential non-linear regression model and dark adaptation indices derived. The results were compared to age-related values.Results: The mean cone threshold of the group with RP (4.73 ± 0.19 log units) was significantly greater than that found in the control group (3.69 ± 0.12 log units). The rate of cone dark adaptation in the RP group was not significantly different from that of the control group. The a break in the RP group (6.46 ± 0.70 minutes) was delayed when compared to the control group (4.29 ± 0.21 minutes) and the rate of rod dark adaptation in the RF' group was slower (10 ± 2 per cent per minute) than that of the control group (15 ± 1 per cent per minute).Conclusions: This study has shown that a relatively simple data analysis can provide a more quantitative and intuitive description of dark adaptation rates in people with retinal disease. This technique will enable more effective use of dark adaptometry as a supplement to objective electrophysiology, when monitoring people with retinitis pigmentosa.

Bytecode instrumentation is a widely used technique to implement aspect weaving and dynamic analyses in virtual machines such as the Java Virtual Machine. Aspect weavers and other instrumentations are usually developed independently and combining them often requires significant engineering effort, if at all possible. In this paper we introduce polymorphic bytecode instrumentation (PBI), a simple but effective technique that allows