Towards the development of a complete GP system on an FPGA using geometric semantic operators
Created by W.Langdon from
gp-bibliography.bib Revision:1.5776
- @InProceedings{goribar-jimenez:2017:CEC,
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author = "Carlos Goribar-Jimenez and Yazmin Maldonado and
Leonardo Trujillo and Mauro Castelli and
Ivo Goncalves and Leonardo Vanneschi",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Towards the development of a complete GP system on an
FPGA using geometric semantic operators",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "1932--1939",
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address = "Donostia, San Sebastian, Spain",
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publisher = "IEEE",
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isbn13 = "978-1-5090-4601-0",
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abstract = "Genetic Programming (GP) has been around for over two
decades and has been used in a wide range of practical
applications producing human competitive results in
several domains. In this paper we present a discussion
and a proposal of a GP algorithm that could be
conveniently implemented on an embedded system, as part
of a broader research project that pursues the
implementation of a complete GP system in a Field
Programmable Gate Array (FPGA). Motivated by the
significant time savings associated with such a
platform, as well as low power consumption, low
maintenance requirements, small size of the system and
the possibility of performing several parallel
processes. The proposal is focused on the Geometric
Semantic Genetic Programming (GSGP) approach that has
been recently introduced with promising results. GSGP
induces a unimodal fitness landscape, simplifying the
search process. The experimental work considers five
variants of GSGP, that incorporate local search
strategies, optimal mutations and alignment in error
space. Best results were obtained by a simple variant
that uses both the optimal mutation step and the
standard geometric semantic mutation, using three
difficult real-world problems to evaluate the methods,
outperforming the original GSGP formulation in terms of
fitness and empirical convergence.",
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keywords = "genetic algorithms, genetic programming, convergence,
embedded systems, field programmable gate arrays,
geometry, parallel processing, search problems, FPGA,
GP algorithm, GP system development, GSGP, embedded
system, empirical convergence, error space alignment,
field programmable gate array, geometric semantic
genetic programming, geometric semantic operators,
local search strategies, maintenance requirements,
optimal mutation step, parallel processes, power
consumption, standard geometric semantic mutation, time
savings, unimodal fitness landscape, Arrays, GSM,
Proposals, Semantics, Standards",
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isbn13 = "978-1-5090-4601-0",
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DOI = "
doi:10.1109/CEC.2017.7969537",
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month = "5-8 " # jun,
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969537}",
- }
Genetic Programming entries for
Carlos Antonio Goribar Jimenez
Yazmin Maldonado Robles
Leonardo Trujillo
Mauro Castelli
Ivo Goncalves
Leonardo Vanneschi
Citations