A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems
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- @Article{RiscoMartin2010572,
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author = "Jose L. Risco-Martin and David Atienza and
J. Manuel Colmenar and Oscar Garnica",
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title = "A parallel evolutionary algorithm to optimize dynamic
memory managers in embedded systems",
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journal = "Parallel Computing",
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volume = "36",
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number = "10-11",
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pages = "572--590",
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year = "2010",
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note = "Parallel Architectures and Bioinspired Algorithms",
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ISSN = "0167-8191",
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DOI = "doi:10.1016/j.parco.2010.07.001",
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URL = "http://www.sciencedirect.com/science/article/B6V12-50J9GPR-1/2/e049c72f4c9e284bd1c2bdbf7c09f3aa",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, genetic improvement, garbage collection,
SBSE, Embedded systems design, Dynamic memory
management, Evolutionary computation, Distributed
simulation",
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abstract = "For the last 30 years, several dynamic memory managers
(DMMs) have been proposed. Such DMMs include first fit,
best fit, segregated fit and buddy systems. Since the
performance, memory usage and energy consumption of
each DMM differs, software engineers often face
difficult choices in selecting the most suitable
approach for their applications. This issue has special
impact in the field of portable consumer embedded
systems, that must execute a limited amount of
multimedia applications (e.g., 3D games, video players,
signal processing software, etc.), demanding high
performance and extensive memory usage at a low energy
consumption. Recently, we have developed a novel
methodology based on genetic programming to
automatically design custom DMMs, optimising
performance, memory usage and energy consumption.
However, although this process is automatic and faster
than state-of-the-art optimizations, it demands
intensive computation, resulting in a time-consuming
process. Thus, parallel processing can be very useful
to enable to explore more solutions spending the same
time, as well as to implement new algorithms. In this
paper we present a novel parallel evolutionary
algorithm for DMMs optimisation in embedded systems,
based on the Discrete Event Specification (DEVS)
formalism over a Service Oriented Architecture (SOA)
framework. Parallelism significantly improves the
performance of the sequential exploration algorithm. On
the one hand, when the number of generations are the
same in both approaches, our parallel optimization
framework is able to reach a speed-up of 86.40times
when compared with other state-of-the-art approaches.
On the other, it improves the global quality (i.e.,
level of performance, low memory usage and low energy
consumption) of the final DMM obtained in a
36.36percent with respect to two well-known
general-purpose DMMs and two state-of-the-art
optimisation methodologies.",
- }
Genetic Programming entries for
Jose L Risco-Martin
David Atienza Alonso
J Manuel Colmenar
Oscar Garnica
Citations