230 research outputs found
Evolution of genetic organization in digital organisms
We examine the evolution of expression patterns and the organization of
genetic information in populations of self-replicating digital organisms.
Seeding the experiments with a linearly expressed ancestor, we witness the
development of complex, parallel secondary expression patterns. Using
principles from information theory, we demonstrate an evolutionary pressure
towards overlapping expressions causing variation (and hence further evolution)
to sharply drop. Finally, we compare the overlapping sections of dominant
genomes to those portions which are singly expressed and observe a significant
difference in the entropy of their encoding.Comment: 18 pages with 5 embedded figures. Proc. of DIMACS workshop on
"Evolution as Computation", Jan. 11-12, Princeton, NJ. L. Landweber and E.
Winfree, eds. (Springer, 1999
Avida: a software platform for research in computational evolutionary biology
Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed
Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures
We investigate fundamental decisions in the design of instruction set
architectures for linear genetic programs that are used as both model systems
in evolutionary biology and underlying solution representations in evolutionary
computation. We subjected digital organisms with each tested architecture to
seven different computational environments designed to present a range of
evolutionary challenges. Our goal was to engineer a general purpose
architecture that would be effective under a broad range of evolutionary
conditions. We evaluated six different types of architectural features for the
virtual CPUs: (1) genetic flexibility: we allowed digital organisms to more
precisely modify the function of genetic instructions, (2) memory: we provided
an increased number of registers in the virtual CPUs, (3) decoupled sensors and
actuators: we separated input and output operations to enable greater control
over data flow. We also tested a variety of methods to regulate expression: (4)
explicit labels that allow programs to dynamically refer to specific genome
positions, (5) position-relative search instructions, and (6) multiple new flow
control instructions, including conditionals and jumps. Each of these features
also adds complication to the instruction set and risks slowing evolution due
to epistatic interactions. Two features (multiple argument specification and
separated I/O) demonstrated substantial improvements int the majority of test
environments. Some of the remaining tested modifications were detrimental,
thought most exhibit no systematic effects on evolutionary potential,
highlighting the robustness of digital evolution. Combined, these observations
enhance our understanding of how instruction architecture impacts evolutionary
potential, enabling the creation of architectures that support more rapid
evolution of complex solutions to a broad range of challenges
Using Avida to test the effects of natural selection on phylogenetic reconstruction methods
Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent
Best-Effort Communication Improves Performance and Scales Robustly on Conventional Hardware
Here, we test the performance and scalability of fully-asynchronous,
best-effort communication on existing, commercially-available HPC hardware.
A first set of experiments tested whether best-effort communication
strategies can benefit performance compared to the traditional perfect
communication model. At high CPU counts, best-effort communication improved
both the number of computational steps executed per unit time and the solution
quality achieved within a fixed-duration run window.
Under the best-effort model, characterizing the distribution of quality of
service across processing components and over time is critical to understanding
the actual computation being performed. Additionally, a complete picture of
scalability under the best-effort model requires analysis of how such quality
of service fares at scale. To answer these questions, we designed and measured
a suite of quality of service metrics: simulation update period, message
latency, message delivery failure rate, and message delivery coagulation. Under
a lower communication-intensivity benchmark parameterization, we found that
median values for all quality of service metrics were stable when scaling from
64 to 256 process. Under maximal communication intensivity, we found only minor
-- and, in most cases, nil -- degradation in median quality of service.
In an additional set of experiments, we tested the effect of an apparently
faulty compute node on performance and quality of service. Despite extreme
quality of service degradation among that node and its clique, median
performance and quality of service remained stable
Exploring Evolved Multicellular Life Histories in a Open-Ended Digital Evolution System
Evolutionary transitions occur when previously-independent replicating
entities unite to form more complex individuals. Such transitions have
profoundly shaped natural evolutionary history and occur in two forms:
fraternal transitions involve lower-level entities that are kin (e.g.,
transitions to multicellularity or to eusocial colonies), while egalitarian
transitions involve unrelated individuals (e.g., the origins of mitochondria).
The necessary conditions and evolutionary mechanisms for these transitions to
arise continue to be fruitful targets of scientific interest. Here, we examine
a range of fraternal transitions in populations of open-ended self-replicating
computer programs. These digital cells were allowed to form and replicate kin
groups by selectively adjoining or expelling daughter cells. The capability to
recognize kin-group membership enabled preferential communication and
cooperation between cells. We repeatedly observed group-level traits that are
characteristic of a fraternal transition. These included reproductive division
of labor, resource sharing within kin groups, resource investment in offspring
groups, asymmetrical behaviors mediated by messaging, morphological patterning,
and adaptive apoptosis. We report eight case studies from replicates where
transitions occurred and explore the diverse range of adaptive evolved
multicellular strategies
Matchmaker, Matchmaker, Make Me a Match: Geometric, Variational, and Evolutionary Implications of Criteria for Tag Affinity
Genetic programming and artificial life systems commonly employ tag-matching
schemes to determine interactions between model components. However, the
implications of criteria used to determine affinity between tags with respect
to constraints on emergent connectivity, canalization of changes to
connectivity under mutation, and evolutionary dynamics have not been
considered. We highlight differences between tag-matching criteria with respect
to geometric constraint and variation generated under mutation. We find that
tag-matching criteria can influence the rate of adaptive evolution and the
quality of evolved solutions. Better understanding of the geometric,
variational, and evolutionary properties of tag-matching criteria will
facilitate more effective incorporation of tag matching into genetic
programming and artificial life systems. By showing that tag-matching criteria
influence connectivity patterns and evolutionary dynamics, our findings also
raise fundamental questions about the properties of tag-matching systems in
nature
The effect of natural selection on the performance of maximum parsimony
<p>Abstract</p> <p>Background</p> <p>Maximum parsimony is one of the most commonly used and extensively studied phylogeny reconstruction methods. While current evaluation methodologies such as computer simulations provide insight into how well maximum parsimony reconstructs phylogenies, they tell us little about how well maximum parsimony performs on taxa drawn from populations of organisms that evolved subject to <it>natural selection </it>in addition to the random factors of drift and mutation. It is clear that natural selection has a significant impact on <it>Among Site Rate Variation </it>(ASRV) and the rate of accepted substitutions; that is, accepted mutations do not occur with uniform probability along the genome and some substitutions are more likely to occur than other substitutions. However, little is know about how ASRV and non-uniform character substitutions impact the performance of reconstruction methods such as maximum parsimony. To gain insight into these issues, we study how well maximum parsimony performs with data generated by Avida, a digital life platform where populations of digital organisms evolve subject to natural selective pressures.</p> <p>Results</p> <p>We first identify conditions where natural selection does affect maximum parsimony's reconstruction accuracy. In general, as we increase the probability that a significant adaptation will occur in an intermediate ancestor, the performance of maximum parsimony improves. In fact, maximum parsimony can correctly reconstruct small 4 taxa trees on data that have received surprisingly many mutations if the intermediate ancestor has received a significant adaptation. We demonstrate that this improved performance of maximum parsimony is attributable more to ASRV than to non-uniform character substitutions.</p> <p>Conclusion</p> <p>Maximum parsimony, as well as most other phylogeny reconstruction methods, may perform significantly better on actual biological data than is currently suggested by computer simulation studies because of natural selection. This is largely due to specific sites becoming fixed in the genome that perform functions associated with an improved fitness.</p
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