2,194 research outputs found
L\'evy flights and self-similar exploratory behaviour of termite workers: beyond model fitting
Animal movements have been related to optimal foraging strategies where
self-similar trajectories are central. Most of the experimental studies done so
far have focused mainly on fitting statistical models to data in order to test
for movement patterns described by power-laws. Here we show by analyzing over
half a million movement displacements that isolated termite workers actually
exhibit a range of very interesting dynamical properties --including L\'evy
flights-- in their exploratory behaviour. Going beyond the current trend of
statistical model fitting alone, our study analyses anomalous diffusion and
structure functions to estimate values of the scaling exponents describing
displacement statistics. We evince the fractal nature of the movement patterns
and show how the scaling exponents describing termite space exploration
intriguingly comply with mathematical relations found in the physics of
transport phenomena. By doing this, we rescue a rich variety of physical and
biological phenomenology that can be potentially important and meaningful for
the study of complex animal behavior and, in particular, for the study of how
patterns of exploratory behaviour of individual social insects may impact not
only their feeding demands but also nestmate encounter patterns and, hence,
their dynamics at the social scale.Comment: 13 pages, 11 figures. Unrevised version. Final version to appear in
Plos ON
Evolution and complexity: the double-edged sword
We attempt to provide a comprehensive answer to the question of whether, and when, an arrow of complexity emerges in Darwinian evolution. We note that this expression can be interpreted in different ways, including a passive, incidental growth, or a pervasive bias towards complexification. We argue at length that an arrow of complexity does indeed occur in evolution, which can be most reasonably interpreted as the result of a passive trend rather than a driven one. What, then, is the role of evolution in the creation of this trend, and under which conditions will it emerge? In the later sections of this article we point out that when certain proper conditions (which we attempt to formulate in a concise form) are met, Darwinian evolution predictably creates a sustained trend of increase in maximum complexity (that is, an arrow of complexity) that would not be possible without it; but if they are not, evolution will not only fail to produce an arrow of complexity, but may actually prevent any increase in complexity altogether. We conclude that, with regard to the growth of complexity, evolution is very much a double-edged sword
Natural Selection, Adaptive Evolution and Diversity in Computational Ecosystems
The central goal of this thesis is to provide additional criteria towards implementing open-ended evolution in an artificial system. Methods inspired by biological evolution are frequently applied to generate autonomous agents too complex to design by hand. Despite substantial progress in the area of evolutionary computation, additional efforts are needed to identify a coherent set of requirements for a system
capable of exhibiting open-ended evolutionary dynamics.
The thesis provides an extensive discussion of existing models and of the major
considerations for designing a computational model of evolution by natural selection.
Thus, the work in this thesis constitutes a further step towards determining
the requirements for such a system and introduces a concrete implementation of
an artificial evolution system to evaluate the developed suggestions. The proposed
system improves upon existing models with respect to easy interpretability of agent
behaviour, high structural freedom, and a low-level sensor and effector model to
allow numerous long-term evolutionary gradients.
In a series of experiments, the evolutionary dynamics of the system are examined
against the set objectives and, where appropriate, compared with existing systems.
Typical agent behaviours are introduced to convey a general overview of the system
dynamics. These behaviours are related to properties of the respective agent populations and their evolved morphologies. It is shown that an intuitive classification of observed behaviours coincides with a more formal classification based on morphology.
The evolutionary dynamics of the system are evaluated and shown to be unbounded according to the classification provided by Bedau and Packard’s measures of evolutionary
activity. Further, it is analysed how observed behavioural complexity relates
to the complexity of the agent-side mechanisms subserving these behaviours. It is
shown that for the concrete definition of complexity applied, the average complexity
continually increases for extended periods of evolutionary time. In combination,
these two findings show how the observed behaviours are the result of an ongoing
and lasting adaptive evolutionary process as opposed to being artifacts of the seeding
process.
Finally, the effect of variation in the system on the diversity of evolved behaviour is investigated. It is shown that coupling individual survival and reproductive success
can restrict the available evolutionary trajectories in more than the trivial sense of removing another dimension, and conversely, decoupling individual survival from reproductive success can increase the number of evolutionary trajectories. The effect of different reproductive mechanisms is contrasted with that of variation in environmental conditions. The diversity of evolved strategies turns out to be sensitive to the reproductive mechanism while being remarkably robust to the variation of environmental conditions. These findings emphasize the importance of being explicit
about the abstractions and assumptions underlying an artificial evolution system,
particularly if the system is intended to model aspects of biological evolution
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Copepods encounter rates from a model of escape jump behaviour in turbulence
A key ecological parameter for planktonic copepods studies is their
interspecies encounter rate which is driven by their behaviour and is strongly
influenced by turbulence of the surrounding environment. A distinctive feature
of copepods motility is their ability to perform quick displacements, often
dubbed jumps, by means of powerful swimming strokes. Such a reaction has been
associated to an escape behaviour from flow disturbances due to predators or
other external dangers. In the present study, the encounter rate of copepods in
a developed turbulent flow with intensity comparable to the one found in
copepods' habitat is numerically investigated. This is done by means of a
Lagrangian copepod (LC) model that mimics the jump escape reaction behaviour
from localised high-shear rate fluctuations in the turbulent flows. Our
analysis shows that the encounter rate for copepods of typical perception
radius of ~ {\eta}, where {\eta} is the dissipative scale of turbulence, can be
increased by a factor up to ~ 100 compared to the one experienced by passively
transported fluid tracers. Furthermore, we address the effect of introducing in
the LC model a minimal waiting time between consecutive jumps. It is shown that
any encounter-rate enhancement is lost if such time goes beyond the dissipative
time-scale of turbulence, {\tau}_{\eta}. Because typically in the ocean {\eta}
~ 0.001m and {\tau}_{\eta} ~ 1s, this provides stringent constraints on the
turbulent-driven enhancement of encounter-rate due to a purely mechanical
induced escape reaction.Comment: 11 pages, 10 figure
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