5 research outputs found
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
Reachability Analysis for Lexicase Selection via Community Assembly Graphs
Fitness landscapes have historically been a powerful tool for analyzing the
search space explored by evolutionary algorithms. In particular, they
facilitate understanding how easily reachable an optimal solution is from a
given starting point. However, simple fitness landscapes are inappropriate for
analyzing the search space seen by selection schemes like lexicase selection in
which the outcome of selection depends heavily on the current contents of the
population (i.e. selection schemes with complex ecological dynamics). Here, we
propose borrowing a tool from ecology to solve this problem: community assembly
graphs. We demonstrate a simple proof-of-concept for this approach on an NK
Landscape where we have perfect information. We then demonstrate that this
approach can be successfully applied to a complex genetic programming problem.
While further research is necessary to understand how to best use this tool, we
believe it will be a valuable addition to our toolkit and facilitate analyses
that were previously impossible
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