304 research outputs found
Semantic variation operators for multidimensional genetic programming
Multidimensional genetic programming represents candidate solutions as sets
of programs, and thereby provides an interesting framework for exploiting
building block identification. Towards this goal, we investigate the use of
machine learning as a way to bias which components of programs are promoted,
and propose two semantic operators to choose where useful building blocks are
placed during crossover. A forward stagewise crossover operator we propose
leads to significant improvements on a set of regression problems, and produces
state-of-the-art results in a large benchmark study. We discuss this
architecture and others in terms of their propensity for allowing heuristic
search to utilize information during the evolutionary process. Finally, we look
at the collinearity and complexity of the data representations that result from
these architectures, with a view towards disentangling factors of variation in
application.Comment: 9 pages, 8 figures, GECCO 201
Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures
Generalized linear mixed-effects models in the context of genome-wide
association studies (GWAS) represent a formidable computational challenge: the
solution of millions of correlated generalized least-squares problems, and the
processing of terabytes of data. We present high performance in-core and
out-of-core shared-memory algorithms for GWAS: By taking advantage of
domain-specific knowledge, exploiting multi-core parallelism, and handling data
efficiently, our algorithms attain unequalled performance. When compared to
GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor
we obtain 50-fold speedups. As a consequence, our routines enable genome
studies of unprecedented size
Partial Evaluation for Scientific Computing: The Supercomputer Toolkit Experience
We describe the key role played by partial evaluation in the Supercomputer Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputer Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at M.I.T., and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable
Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'
In the power and energy systems area, a progressive increase of literature
contributions containing applications of metaheuristic algorithms is occurring.
In many cases, these applications are merely aimed at proposing the testing of
an existing metaheuristic algorithm on a specific problem, claiming that the
proposed method is better than other methods based on weak comparisons. This
'rush to heuristics' does not happen in the evolutionary computation domain,
where the rules for setting up rigorous comparisons are stricter, but are
typical of the domains of application of the metaheuristics. This paper
considers the applications to power and energy systems, and aims at providing a
comprehensive view of the main issues concerning the use of metaheuristics for
global optimization problems. A set of underlying principles that characterize
the metaheuristic algorithms is presented. The customization of metaheuristic
algorithms to fit the constraints of specific problems is discussed. Some
weaknesses and pitfalls found in literature contributions are identified, and
specific guidelines are provided on how to prepare sound contributions on the
application of metaheuristic algorithms to specific problems
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