16,709 research outputs found
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in
the broad field of computational intelligence. Accordingly, a wide repertoire
of pattern recognition tools, such as classifiers and knowledge discovery
procedures, are nowadays available and tested for various datasets of labeled
graphs. However, the design of effective learning procedures operating in the
space of labeled graphs is still a challenging problem, especially from the
computational complexity viewpoint. In this paper, we present a major
improvement of a general-purpose classifier for graphs, which is conceived on
an interplay between dissimilarity representation, clustering,
information-theoretic techniques, and evolutionary optimization algorithms. The
improvement focuses on a specific key subroutine devised to compress the input
data. We prove different theorems which are fundamental to the setting of the
parameters controlling such a compression operation. We demonstrate the
effectiveness of the resulting classifier by benchmarking the developed
variants on well-known datasets of labeled graphs, considering as distinct
performance indicators the classification accuracy, computing time, and
parsimony in terms of structural complexity of the synthesized classification
models. The results show state-of-the-art standards in terms of test set
accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio
An incremental approach to genetic algorithms based classification
Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed
A memetic algorithm for the university course timetabling problem
This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP
Methods for many-objective optimization: an analysis
Decomposition-based methods are often cited as the
solution to problems related with many-objective optimization. Decomposition-based methods employ a scalarizing function to reduce a many-objective problem into a set of single objective problems, which upon solution yields a good approximation of the set of optimal solutions. This set is commonly referred to as
Pareto front. In this work we explore the implications of using decomposition-based methods over Pareto-based methods from a probabilistic point of view. Namely, we investigate whether there is an advantage of using a decomposition-based method, for example using the Chebyshev scalarizing function, over Paretobased methods
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
Incremental multiple objective genetic algorithms
This paper presents a new genetic algorithm approach to multi-objective optimization problemsIncremental Multiple Objective Genetic Algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages: first, an independent population is evolved to optimize one specific objective; second, the better-performing individuals from the evolved single-objective population and the multi-objective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multi-objective population, to which a multi-objective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better
The Single Row Facility Layout Problem: State of the Art
The single row facility layout problem (SRFLP) is a NP-hard problem concerned with the arrangement of facilities of given lenghs on a line so as to minimize the weighted sum of the distances between all the pairs of facilities. The SRFLP and its special cases often arise while modeling a large variety of applications. It was actively researched until the mid-nineties. It has again been actively studied since 2005. Interestingly, research on many aspects of this problem is still in the initial stages, and hence the SRFLP is an interesting problem to work on. In this paper, we review the literature on the SRFLP and comment on its relationship with other location problems. We then provide an overview of different formulations of the problem that appear in the literature. We provide exact and heuristic approaches that have been used to solve SRFLPs, and finally point out research gaps and promising directions for future research on this problem.
Parallel Implementation of Efficient Search Schemes for the Inference of Cancer Progression Models
The emergence and development of cancer is a consequence of the accumulation
over time of genomic mutations involving a specific set of genes, which
provides the cancer clones with a functional selective advantage. In this work,
we model the order of accumulation of such mutations during the progression,
which eventually leads to the disease, by means of probabilistic graphic
models, i.e., Bayesian Networks (BNs). We investigate how to perform the task
of learning the structure of such BNs, according to experimental evidence,
adopting a global optimization meta-heuristics. In particular, in this work we
rely on Genetic Algorithms, and to strongly reduce the execution time of the
inference -- which can also involve multiple repetitions to collect
statistically significant assessments of the data -- we distribute the
calculations using both multi-threading and a multi-node architecture. The
results show that our approach is characterized by good accuracy and
specificity; we also demonstrate its feasibility, thanks to a 84x reduction of
the overall execution time with respect to a traditional sequential
implementation
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