17,699 research outputs found
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
Language: The missing selection pressure
Human beings are talkative. What advantage did their ancestors find in
communicating so much? Numerous authors consider this advantage to be "obvious"
and "enormous". If so, the problem of the evolutionary emergence of language
amounts to explaining why none of the other primate species evolved anything
even remotely similar to language. What I propose here is to reverse the
picture. On closer examination, language resembles a losing strategy. Competing
for providing other individuals with information, sometimes striving to be
heard, makes apparently no sense within a Darwinian framework. At face value,
language as we can observe it should never have existed or should have been
counter-selected. In other words, the selection pressure that led to language
is still missing. The solution I propose consists in regarding language as a
social signaling device that developed in a context of generalized insecurity
that is unique to our species. By talking, individuals advertise their
alertness and their ability to get informed. This hypothesis is shown to be
compatible with many characteristics of language that otherwise are left
unexplained.Comment: 34 pages, 3 figure
The evolutionary origins of hierarchy
Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.Comment: 32 page
Interplay of spatial dynamics and local adaptation shapes species lifetime distributions and species-area relationships
The distributions of species lifetimes and species in space are related,
since species with good local survival chances have more time to colonize new
habitats and species inhabiting large areas have higher chances to survive
local disturbances. Yet, both distributions have been discussed in mostly
separate communities. Here, we study both patterns simultaneously using a
spatially explicit, evolutionary community assembly approach. We present and
investigate a metacommunity model, consisting of a grid of patches, where each
patch contains a local food web. Species survival depends on predation and
competition interactions, which in turn depend on species body masses as the
key traits. The system evolves due to the migration of species to neighboring
patches, the addition of new species as modifications of existing species, and
local extinction events. The structure of each local food web thus emerges in a
self-organized manner as the highly non-trivial outcome of the relative time
scales of these processes. Our model generates a large variety of complex,
multi-trophic networks and therefore serves as a powerful tool to investigate
ecosystems on long temporal and large spatial scales. We find that the observed
lifetime distributions and species-area relations resemble power laws over
appropriately chosen parameter ranges and thus agree qualitatively with
empirical findings. Moreover, we observe strong finite-size effects, and a
dependence of the relationships on the trophic level of the species. By
comparing our results to simple neutral models found in the literature, we
identify the features that are responsible for the values of the exponents.Comment: Theor Ecol (2019
On the emergence and evolution of artificial cell signaling networks
This PhD project is concerned with the evolution of Cell
Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We are investigating the possibility to build an evolutionary simulation platform that would allow the spontaneous emergence and evolution of Artificial Cell Signaling Networks (ACSNs). From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. This work may also contribute to the biological understanding of the origins and evolution of real CSNs
Resolving social dilemmas on evolving random networks
We show that strategy independent adaptations of random interaction networks
can induce powerful mechanisms, ranging from the Red Queen to group selection,
that promote cooperation in evolutionary social dilemmas. These two mechanisms
emerge spontaneously as dynamical processes due to deletions and additions of
links, which are performed whenever players adopt new strategies and after a
certain number of game iterations, respectively. The potency of cooperation
promotion, as well as the mechanism responsible for it, can thereby be tuned
via a single parameter determining the frequency of link additions. We thus
demonstrate that coevolving random networks may evoke an appropriate mechanism
for each social dilemma, such that cooperation prevails even by highly
unfavorable conditions.Comment: 6 two-column pages, 7 figures; accepted for publication in
Europhysics Letter
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