Genetic Improvement: A Key Challenge for Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "William B. Langdon and Gabriela Ochoa",
  title =        "Genetic Improvement: A Key Challenge for Evolutionary
  booktitle =    "Key Challenges and Future Directions of Evolutionary
  year =         "2016",
  editor =       "Yun Li",
  pages =        "3068--3075",
  address =      "Vancouver",
  month =        "25-29 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, genetic
  isbn13 =       "978-1-5090-0623-6",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2016.7744177",
  size =         "8 pages",
  abstract =     "Automatic Programming has long been a sub-goal of
                 Artificial Intelligence (AI). It is feasible in limited
                 domains. Genetic Improvement (GI) has expanded these
                 dramatically to more than 100000 lines of code by
                 building on human written applications. Further scaling
                 may need key advances in both Search Based Software
                 Engineering (SBSE) and Evolutionary Computation (EC)
                 research, particularly on representations, genetic
                 operations, fitness landscapes, fitness surrogates,
                 multi objective search and co-evolution.",
  notes =        "Slides

                 Paper ID 17175. GISMO


Genetic Programming entries for William B Langdon Gabriela Ochoa