4,322 research outputs found
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
We focus on the challenge of finding a diverse collection of quality
solutions on complex continuous domains. While quality diver-sity (QD)
algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are
designed to generate a diverse range of solutions, these algorithms require a
large number of evaluations for exploration of continuous spaces. Meanwhile,
variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are
among the best-performing derivative-free optimizers in single-objective
continuous domains. This paper proposes a new QD algorithm called Covariance
Matrix Adaptation MAP-Elites (CMA-ME). Our new algorithm combines the
self-adaptation techniques of CMA-ES with archiving and mapping techniques for
maintaining diversity in QD. Results from experiments based on standard
continuous optimization benchmarks show that CMA-ME finds better-quality
solutions than MAP-Elites; similarly, results on the strategic game Hearthstone
show that CMA-ME finds both a higher overall quality and broader diversity of
strategies than both CMA-ES and MAP-Elites. Overall, CMA-ME more than doubles
the performance of MAP-Elites using standard QD performance metrics. These
results suggest that QD algorithms augmented by operators from state-of-the-art
optimization algorithms can yield high-performing methods for simultaneously
exploring and optimizing continuous search spaces, with significant
applications to design, testing, and reinforcement learning among other
domains.Comment: Accepted to GECCO 202
Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network
Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.Comment: Accepted to AAAI 202
Multi-camera Realtime 3D Tracking of Multiple Flying Animals
Automated tracking of animal movement allows analyses that would not
otherwise be possible by providing great quantities of data. The additional
capability of tracking in realtime - with minimal latency - opens up the
experimental possibility of manipulating sensory feedback, thus allowing
detailed explorations of the neural basis for control of behavior. Here we
describe a new system capable of tracking the position and body orientation of
animals such as flies and birds. The system operates with less than 40 msec
latency and can track multiple animals simultaneously. To achieve these
results, a multi target tracking algorithm was developed based on the Extended
Kalman Filter and the Nearest Neighbor Standard Filter data association
algorithm. In one implementation, an eleven camera system is capable of
tracking three flies simultaneously at 60 frames per second using a gigabit
network of nine standard Intel Pentium 4 and Core 2 Duo computers. This
manuscript presents the rationale and details of the algorithms employed and
shows three implementations of the system. An experiment was performed using
the tracking system to measure the effect of visual contrast on the flight
speed of Drosophila melanogaster. At low contrasts, speed is more variable and
faster on average than at high contrasts. Thus, the system is already a useful
tool to study the neurobiology and behavior of freely flying animals. If
combined with other techniques, such as `virtual reality'-type computer
graphics or genetic manipulation, the tracking system would offer a powerful
new way to investigate the biology of flying animals.Comment: pdfTeX using libpoppler 3.141592-1.40.3-2.2 (Web2C 7.5.6), 18 pages
with 9 figure
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Evolution has produced an astonishing diversity of species, each filling a
different niche. Algorithms like MAP-Elites mimic this divergent evolutionary
process to find a set of behaviorally diverse but high-performing solutions,
called the elites. Our key insight is that species in nature often share a
surprisingly large part of their genome, in spite of occupying very different
niches; similarly, the elites are likely to be concentrated in a specific
"elite hypervolume" whose shape is defined by their common features. In this
paper, we first introduce the elite hypervolume concept and propose two metrics
to characterize it: the genotypic spread and the genotypic similarity. We then
introduce a new variation operator, called "directional variation", that
exploits interspecies (or inter-elites) correlations to accelerate the
MAP-Elites algorithm. We demonstrate the effectiveness of this operator in
three problems (a toy function, a redundant robotic arm, and a hexapod robot).Comment: In GECCO 201
Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm
The recently introduced Multi-dimensional Archive of Phenotypic Elites
(MAP-Elites) is an evolutionary algorithm capable of producing a large archive
of diverse, high-performing solutions in a single run. It works by discretizing
a continuous feature space into unique regions according to the desired
discretization per dimension. While simple, this algorithm has a main drawback:
it cannot scale to high-dimensional feature spaces since the number of regions
increase exponentially with the number of dimensions. In this paper, we address
this limitation by introducing a simple extension of MAP-Elites that has a
constant, pre-defined number of regions irrespective of the dimensionality of
the feature space. Our main insight is that methods from computational geometry
could partition a high-dimensional space into well-spread geometric regions. In
particular, our algorithm uses a centroidal Voronoi tessellation (CVT) to
divide the feature space into a desired number of regions; it then places every
generated individual in its closest region, replacing a less fit one if the
region is already occupied. We demonstrate the effectiveness of the new
"CVT-MAP-Elites" algorithm in high-dimensional feature spaces through
comparisons against MAP-Elites in maze navigation and hexapod locomotion tasks
Insulin secretory granules labelled with phogrin-fluorescent proteins show alterations in size, mobility and responsiveness to glucose stimulation in living β-cells
The intracellular life of insulin secretory granules (ISGs) from biogenesis to secretion depends on their structural (e.g. size) and dynamic (e.g. diffusivity, mode of motion) properties. Thus, it would be useful to have rapid and robust measurements of such parameters in living β-cells. To provide such measurements, we have developed a fast spatiotemporal fluctuation spectroscopy. We calculate an imaging-derived Mean Squared Displacement (iMSD), which simultaneously provides the size, average diffusivity, and anomalous coefficient of ISGs, without the need to extract individual trajectories. Clustering of structural and dynamic quantities in a multidimensional parametric space defines the ISGs’ properties for different conditions. First, we create a reference using INS-1E cells expressing proinsulin fused to a fluorescent protein (FP) under basal culture conditions and validate our analysis by testing well-established stimuli, such as glucose intake, cytoskeleton disruption, or cholesterol overload. After, we investigate the effect of FP-tagged ISG protein markers on the structural and dynamic properties of the granule. While iMSD analysis produces similar results for most of the lumenal markers, the transmembrane marker phogrin-FP shows a clearly altered result. Phogrin overexpression induces a substantial granule enlargement and higher mobility, together with a partial de-polymerization of the actin cytoskeleton, and reduced cell responsiveness to glucose stimulation. Our data suggest a more careful interpretation of many previous ISG-based reports in living β-cells. The presented data pave the way to high-throughput cell-based screening of ISG structure and dynamics under various physiological and pathological conditions
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
Complex engineering design problems, such as those involved in aerospace,
civil, or energy engineering, require the use of numerically costly simulation
codes in order to predict the behavior and performance of the system to be
designed. To perform the design of the systems, these codes are often embedded
into an optimization process to provide the best design while satisfying the
design constraints. Recently, new approaches, called Quality-Diversity, have
been proposed in order to enhance the exploration of the design space and to
provide a set of optimal diversified solutions with respect to some feature
functions. These functions are interesting to assess trade-offs. Furthermore,
complex engineering design problems often involve mixed continuous, discrete,
and categorical design variables allowing to take into account technological
choices in the optimization problem. In this paper, a new Quality-Diversity
methodology based on mixed continuous, discrete and categorical Bayesian
optimization strategy is proposed. This approach allows to reduce the
computational cost with respect to classical Quality - Diversity approaches
while dealing with discrete choices and constraints. The performance of the
proposed method is assessed on a benchmark of analytical problems as well as on
an industrial design optimization problem dealing with aerospace systems
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