1,960 research outputs found
A unified race algorithm for offline parameter tuning
This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods
First Evaluation of the CPU, GPGPU and MIC Architectures for Real Time Particle Tracking based on Hough Transform at the LHC
Recent innovations focused around {\em parallel} processing, either through
systems containing multiple processors or processors containing multiple cores,
hold great promise for enhancing the performance of the trigger at the LHC and
extending its physics program. The flexibility of the CMS/ATLAS trigger system
allows for easy integration of computational accelerators, such as NVIDIA's
Tesla Graphics Processing Unit (GPU) or Intel's \xphi, in the High Level
Trigger. These accelerators have the potential to provide faster or more energy
efficient event selection, thus opening up possibilities for new complex
triggers that were not previously feasible. At the same time, it is crucial to
explore the performance limits achievable on the latest generation multicore
CPUs with the use of the best software optimization methods. In this article, a
new tracking algorithm based on the Hough transform will be evaluated for the
first time on a multi-core Intel Xeon E5-2697v2 CPU, an NVIDIA Tesla K20c GPU,
and an Intel \xphi\ 7120 coprocessor. Preliminary time performance will be
presented.Comment: 13 pages, 4 figures, Accepted to JINS
Learning to Control Differential Evolution Operators
Evolutionary algorithms are widely used for optimsation by researchers in academia and industry. These algorithms have parameters, which have proven to highly determine the performance of an algorithm. For many decades, researchers have focused on determining optimal parameter values for an algorithm. Each parameter configuration has a performance value attached to it that is used to determine a good configuration for an algorithm. Parameter values depend on the problem at hand and are known to be set in two ways, by means of offline and online selection. Offline tuning assumes that the performance value of a configuration remains same during all generations in a run whereas online tuning assumes that the performance value varies from one generation to another.
This thesis presents various adaptive approaches each learning from a range of feedback received from the evolutionary algorithm. The contributions demonstrate the benefits of utilising online and offline learning together at different levels for a particular task. Offline selection has been utilised to tune the hyper-parameters of proposed adaptive methods that control the parameters of evolutionary algorithm on-the-fly. All the contributions have been presented to control the mutation strategies of the differential evolution. The first contribution demonstrates an adaptive method that is mapped as markov reward process. It aims to maximise the cumulative future reward. Next chapter unifies various adaptive methods from literature that can be utilised to replicate existing methods and test new ones. The hyper-parameters of methods in first two chapters are tuned by an offline configurator, irace. Last chapter proposes four methods utilising deep reinforcement learning model. To test the applicability of the adaptive approaches presented in the thesis, all methods are compared to various adaptive methods from literature, variants of differential evolution and other state-of-the-art algorithms on various single objective noiseless problems from benchmark set, BBOB
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 Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems
Recent technological advances have greatly improved the performance and
features of embedded systems. With the number of just mobile devices now
reaching nearly equal to the population of earth, embedded systems have truly
become ubiquitous. These trends, however, have also made the task of managing
their power consumption extremely challenging. In recent years, several
techniques have been proposed to address this issue. In this paper, we survey
the techniques for managing power consumption of embedded systems. We discuss
the need of power management and provide a classification of the techniques on
several important parameters to highlight their similarities and differences.
This paper is intended to help the researchers and application-developers in
gaining insights into the working of power management techniques and designing
even more efficient high-performance embedded systems of tomorrow
Reactive approach for automating exploration and exploitation in ant colony optimization
Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
To achieve a low computational cost when performing online metric learning
for large-scale data, we present a one-pass closed-form solution namely OPML in
this paper. Typically, the proposed OPML first adopts a one-pass triplet
construction strategy, which aims to use only a very small number of triplets
to approximate the representation ability of whole original triplets obtained
by batch-manner methods. Then, OPML employs a closed-form solution to update
the metric for new coming samples, which leads to a low space (i.e., )
and time (i.e., ) complexity, where is the feature dimensionality.
In addition, an extension of OPML (namely COPML) is further proposed to enhance
the robustness when in real case the first several samples come from the same
class (i.e., cold start problem). In the experiments, we have systematically
evaluated our methods (OPML and COPML) on three typical tasks, including UCI
data classification, face verification, and abnormal event detection in videos,
which aims to fully evaluate the proposed methods on different sample number,
different feature dimensionalities and different feature extraction ways (i.e.,
hand-crafted and deeply-learned). The results show that OPML and COPML can
obtain the promising performance with a very low computational cost. Also, the
effectiveness of COPML under the cold start setting is experimentally verified.Comment: 12 page
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