12,053 research outputs found
ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitful applications in a number of domains have resulted in a large amount
of data, but the community lacks a standard format or repository for this data.
This situation makes it difficult to share and compare different approaches
effectively, as is done in other, more established fields. It also
unnecessarily hinders new researchers who want to work in this area. To address
this problem, we introduce a standardized format for representing algorithm
selection scenarios and a repository that contains a growing number of data
sets from the literature. Our format has been designed to be able to express a
wide variety of different scenarios. Demonstrating the breadth and power of our
platform, we describe a set of example experiments that build and evaluate
algorithm selection models through a common interface. The results display the
potential of algorithm selection to achieve significant performance
improvements across a broad range of problems and algorithms.Comment: Accepted to be published in Artificial Intelligence Journa
Parallelizing a SAT-Based Product Configurator
This paper presents how state-of-the-art parallel algorithms designed to solve the Satisfiability (SAT) problem can be applied in the domain of product configuration. During an interactive configuration process, a user selects features step-by-step to find a suitable configuration that fulfills his desires and the set of product constraints. A configuration system can be used to guide the user through the process by validating the selections and providing feedback. Each validation of a user selection is formulated as a SAT problem. Furthermore, an optimization problem is identified to find solutions with the minimum amount of changes compared to the previous configuration. Another additional constraint is deterministic computation, which is not trivial to achieve in well performing parallel SAT solvers. In the paper we propose five new deterministic parallel algorithms and experimentally compare them. Experiments show that reasonable speedups are achieved by using multiple threads over the sequential counterpart
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
The optimization of algorithm (hyper-)parameters is crucial for achieving
peak performance across a wide range of domains, ranging from deep neural
networks to solvers for hard combinatorial problems. The resulting algorithm
configuration (AC) problem has attracted much attention from the machine
learning community. However, the proper evaluation of new AC procedures is
hindered by two key hurdles. First, AC benchmarks are hard to set up. Second
and even more significantly, they are computationally expensive: a single run
of an AC procedure involves many costly runs of the target algorithm whose
performance is to be optimized in a given AC benchmark scenario. One common
workaround is to optimize cheap-to-evaluate artificial benchmark functions
(e.g., Branin) instead of actual algorithms; however, these have different
properties than realistic AC problems. Here, we propose an alternative
benchmarking approach that is similarly cheap to evaluate but much closer to
the original AC problem: replacing expensive benchmarks by surrogate benchmarks
constructed from AC benchmarks. These surrogate benchmarks approximate the
response surface corresponding to true target algorithm performance using a
regression model, and the original and surrogate benchmark share the same
(hyper-)parameter space. In our experiments, we construct and evaluate
surrogate benchmarks for hyperparameter optimization as well as for AC problems
that involve performance optimization of solvers for hard combinatorial
problems, drawing training data from the runs of existing AC procedures. We
show that our surrogate benchmarks capture overall important characteristics of
the AC scenarios, such as high- and low-performing regions, from which they
were derived, while being much easier to use and orders of magnitude cheaper to
evaluate
A Reference Framework for Variability Management of Software Product Lines
Variability management (VM) in software product line engineering (SPLE) is
introduced as an abstraction that enables the reuse and customization of
assets. VM is a complex task involving the identification, representation, and
instantiation of variability for specific products, as well as the evolution of
variability itself. This work presents a comparison and contrast between
existing VM approaches using qualitative meta-synthesis to determine the
underlying perspectives, metaphors, and concepts of existing methods. A common
frame of reference for the VM was proposed as the result of this analysis.
Putting metaphors in the context of the dimensions in which variability occurs
and identifying its key concepts provides a better understanding of its
management and enables several analyses and evaluation opportunities. Finally,
the proposed framework was evaluated using a qualitative study approach. The
results of the evaluation phase suggest that the organizations in practice only
focus on one dimension. The presented frame of reference will help the
organization to cover this gap in practice.Comment: 24 page
Complexity Management to design and produce customerspecific hydraulic controls for mobile applications
Complexity management is the key to success for mobile machinery where the variety of customers and applications requires individual solutions. This paper presents the way Bosch Rexroth supports each OEM with hydraulic controls – from specification and conception towards application and production. It gives examples how platforms and processes are optimized according to the customer needs. The demand for flexible, short-term deliveries is met by an agile production with the technologies of Industry 4.0
COEL: A Web-based Chemistry Simulation Framework
The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic
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