20,650 research outputs found
In silico transitions to multicellularity
The emergence of multicellularity and developmental programs are among the
major problems of evolutionary biology. Traditionally, research in this area
has been based on the combination of data analysis and experimental work on one
hand and theoretical approximations on the other. A third possibility is
provided by computer simulation models, which allow to both simulate reality
and explore alternative possibilities. These in silico models offer a powerful
window to the possible and the actual by means of modeling how virtual cells
and groups of cells can evolve complex interactions beyond a set of isolated
entities. Here we present several examples of such models, each one
illustrating the potential for artificial modeling of the transition to
multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to
multicellular life (Springer
Global distributed evolution of L-systems fractals
Internet based parallel genetic programming (GP) creates
fractal patterns like Koch’s snow flake.
Pfeiffer, http://www.cs.ucl.ac.uk/staff/W.Langdon/pfeiffer.html,
by analogy with seed/embryo development, uses Lindenmayer grammars
and LOGO style turtle graphics written in Javascript and Perl. 298 novel
pictures were produced. Images are placed in animated snow globes (computerised
snowstorms) by www web browsers anywhere on the planet.
We discuss artificial life (Alife) evolving autonomous agents and virtual
creatures in higher dimensions from a free format representation in the
context of neutral networks, gene duplication and the evolution of higher
order genetic operators
Open-ended search for environments and adapted agents using MAP-Elites
Creatures in the real world constantly encounter new and diverse challenges
they have never seen before. They will often need to adapt to some of these
tasks and solve them in order to survive. This almost endless world of novel
challenges is not as common in virtual environments, where artificially
evolving agents often have a limited set of tasks to solve. An exception to
this is the field of open-endedness where the goal is to create unbounded
exploration of interesting artefacts. We want to move one step closer to
creating simulated environments similar to the diverse real world, where agents
can both find solvable tasks, and adapt to them. Through the use of MAP-Elites
we create a structured repertoire, a map, of terrains and virtual creatures
that locomote through them. By using novelty as a dimension in the grid, the
map can continuously develop to encourage exploration of new environments. The
agents must adapt to the environments found, but can also search for
environments within each cell of the grid to find the one that best fits their
set of skills. Our approach combines the structure of MAP-Elites, which can
allow the virtual creatures to use adjacent cells as stepping stones to solve
increasingly difficult environments, with open-ended innovation. This leads to
a search that is unbounded, but still has a clear structure. We find that while
handcrafted bounded dimensions for the map lead to quicker exploration of a
large set of environments, both the bounded and unbounded approach manage to
solve a diverse set of terrains
Evolving a Behavioral Repertoire for a Walking Robot
Numerous algorithms have been proposed to allow legged robots to learn to
walk. However, the vast majority of these algorithms is devised to learn to
walk in a straight line, which is not sufficient to accomplish any real-world
mission. Here we introduce the Transferability-based Behavioral Repertoire
Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that
simultaneously discovers several hundreds of simple walking controllers, one
for each possible direction. By taking advantage of solutions that are usually
discarded by evolutionary processes, TBR-Evolution is substantially faster than
independently evolving each controller. Our technique relies on two methods:
(1) novelty search with local competition, which searches for both
high-performing and diverse solutions, and (2) the transferability approach,
which com-bines simulations and real tests to evolve controllers for a physical
robot. We evaluate this new technique on a hexapod robot. Results show that
with only a few dozen short experiments performed on the robot, the algorithm
learns a repertoire of con-trollers that allows the robot to reach every point
in its reachable space. Overall, TBR-Evolution opens a new kind of learning
algorithm that simultaneously optimizes all the achievable behaviors of a
robot.Comment: 33 pages; Evolutionary Computation Journal 201
Scalable Co-Optimization of Morphology and Control in Embodied Machines
Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition
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