63,738 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
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
Metamodel Instance Generation: A systematic literature review
Modelling and thus metamodelling have become increasingly important in
Software Engineering through the use of Model Driven Engineering. In this paper
we present a systematic literature review of instance generation techniques for
metamodels, i.e. the process of automatically generating models from a given
metamodel. We start by presenting a set of research questions that our review
is intended to answer. We then identify the main topics that are related to
metamodel instance generation techniques, and use these to initiate our
literature search. This search resulted in the identification of 34 key papers
in the area, and each of these is reviewed here and discussed in detail. The
outcome is that we are able to identify a knowledge gap in this field, and we
offer suggestions as to some potential directions for future research.Comment: 25 page
BigraphER: rewriting and analysis engine for bigraphs
BigraphER is a suite of open-source tools providing an effi-
cient implementation of rewriting, simulation, and visualisation for bigraphs,
a universal formalism for modelling interacting systems that
evolve in time and space and first introduced by Milner. BigraphER consists
of an OCaml library that provides programming interfaces for the
manipulation of bigraphs, their constituents and reaction rules, and a
command-line tool capable of simulating Bigraphical Reactive Systems
(BRSs) and computing their transition systems. Other features are native
support for both bigraphs and bigraphs with sharing, stochastic reaction
rules, rule priorities, instantiation maps, parameterised controls, predicate
checking, graphical output and integration with the probabilistic
model checker PRISM
Approaches to Interpreter Composition
In this paper, we compose six different Python and Prolog VMs into 4 pairwise
compositions: one using C interpreters; one running on the JVM; one using
meta-tracing interpreters; and one using a C interpreter and a meta-tracing
interpreter. We show that programs that cross the language barrier frequently
execute faster in a meta-tracing composition, and that meta-tracing imposes a
significantly lower overhead on composed programs relative to mono-language
programs.Comment: 33 pages, 1 figure, 9 table
LLAMA: Leveraging Learning to Automatically Manage Algorithms
Algorithm portfolio and selection approaches have achieved remarkable
improvements over single solvers. However, the implementation of such systems
is often highly customised and specific to the problem domain. This makes it
difficult for researchers to explore different techniques for their specific
problems. We present LLAMA, a modular and extensible toolkit implemented as an
R package that facilitates the exploration of a range of different portfolio
techniques on any problem domain. It implements the algorithm selection
approaches most commonly used in the literature and leverages the extensive
library of machine learning algorithms and techniques in R. We describe the
current capabilities and limitations of the toolkit and illustrate its usage on
a set of example SAT problems
A nonmonotone GRASP
A greedy randomized adaptive search procedure (GRASP) is an itera-
tive multistart metaheuristic for difficult combinatorial optimization problems. Each
GRASP iteration consists of two phases: a construction phase, in which a feasible
solution is produced, and a local search phase, in which a local optimum in the
neighborhood of the constructed solution is sought. Repeated applications of the con-
struction procedure yields different starting solutions for the local search and the
best overall solution is kept as the result. The GRASP local search applies iterative
improvement until a locally optimal solution is found. During this phase, starting from
the current solution an improving neighbor solution is accepted and considered as the
new current solution. In this paper, we propose a variant of the GRASP framework that
uses a new “nonmonotone” strategy to explore the neighborhood of the current solu-
tion. We formally state the convergence of the nonmonotone local search to a locally
optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP
on three classical hard combinatorial optimization problems: the maximum cut prob-
lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and
the quadratic assignment problem (QAP)
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