Genetic Programming: From design to improved implementation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Lopez:2016:GI,
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author = "Victor R. Lopez-Lopez and Leonardo Trujillo and
Pierrick Legrand and Gustavo Olague",
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title = "Genetic Programming: From design to improved
implementation",
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booktitle = "Genetic Improvement 2016 Workshop",
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year = "2016",
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editor = "Justyna Petke and David R. White and Westley Weimer",
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pages = "1147--1154",
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address = "Denver",
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publisher_address = "New York, NY, USA",
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month = jul # " 20-24",
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organisation = "SIGEvo",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, Genetic
Improvement, SBSE, computer vision",
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URL = "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Genetic_Programming_From_Design_to_Improved_Implementation.pdf",
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DOI = "doi:10.1145/2908961.2931693",
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size = "8 pages",
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abstract = "Genetic programming (GP) is an evolutionary-based
search paradigm that is well suited to automatically
solve difficult design problems. The general principles
of GP have been used to evolve mathematical functions,
models, image operators, programs, and even antennas
and lenses. Since GP evolves the syntax and structure
of a solution, the evolutionary process can be carried
out in one environment and the solution can then be
ported to another. However, given the nature of GP it
is common that the evolved designs are unorthodox
compared to traditional approaches used in the problem
domain. Therefore, efficiently porting, improving or
optimizing an evolved design might not be a trivial
task. In this work we argue that the same GP principles
used to evolve the solution can then be used to
optimize a particular new implementation of the design,
following the Genetic Improvement approach. In
particular, this paper presents a case study where
evolved image operators are ported from Matlab to
OpenCV, and then the source code is optimized an
improved using Genetic Improvement of Software for
Multiple Objectives (GISMOE). In the example we show
that functional behaviour is maintained (output image)
while improving non-functional properties (computation
time). Despite the fact that this first example is a
simple case, it clearly illustrates the possibilities
of using GP principles in two distinct stages of the
software development process, from design to improved
implementation.",
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notes = "GPLAB MATLAB,
http://www.cs.ucl.ac.uk/staff/ucacbbl/gismo/
http://www.tree-lab.org
Fitness from normalized cross correlation and run time
on one test case. pop size=10. 21 percent faster by
discarding 3 operations GISMOE
GECCO-2016 Workshop
http://geneticimprovementofsoftware.com/",
- }
Genetic Programming entries for
Victor Raul Lopez Lopez
Leonardo Trujillo
Pierrick Legrand
Gustavo Olague
Citations