Genetic Programming: From design to improved implementation

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Lopez:2016:GI,
  author =       "Victor R. Lopez-Lopez and Leonardo Trujillo and 
                 Pierrick Legrand and Gustavo Olague",
  title =        "Genetic Programming: From design to improved
                 implementation",
  booktitle =    "Genetic Improvement 2016 Workshop",
  year =         "2016",
  editor =       "Justyna Petke and David R. White and Westley Weimer",
  pages =        "1147--1154",
  address =      "Denver",
  publisher_address = "New York, NY, USA",
  month =        jul # " 20-24",
  organisation = "SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, computer vision",
  URL =          "http://geneticimprovementofsoftware.com/wp-content/uploads/2016/06/Genetic_Programming_From_Design_to_Improved_Implementation.pdf",
  DOI =          "doi:10.1145/2908961.2931693",
  size =         "8 pages",
  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.",
  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