SASS: Self-adaptation using stochastic search

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Zack Coker and David Garlan and Claire {Le Goues}",
  title =        "SASS: Self-adaptation using stochastic search",
  booktitle =    "10th International Symposium on Software Engineering
                 for Adaptive and Self-Managing Systems",
  year =         "2015",
  editor =       "Gerardo Canfora and Sebastian Elbaum and 
                 Antonia Bertolino",
  pages =        "168--174",
  address =      "Florence Italy",
  month =        may # " 18-19",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  isbn13 =       "978-0-7695-5567-6",
  URL =          "",
  DOI =          "doi:10.1109/SEAMS.2015.16",
  size =         "7 pages",
  abstract =     "Future-generation self-adaptive systems will need to
                 be able to optimise for multiple interrelated,
                 difficult to measure, and evolving quality properties.
                 To navigate this complex search space, current
                 self-adaptive planning techniques need to be improved.
                 In this position paper, we argue that the research
                 community should more directly pursue the application
                 of stochastic search techniques, such as hill climbing
                 or genetic algorithms, that incorporate an element of
                 randomness to self-adaptive systems research. These
                 techniques are well-suited to handling
                 multi-dimensional search spaces and complex problems,
                 situations which arise often for self-adaptive systems.
                 We believe that recent advances in both fields make
                 this a particularly promising research trajectory. We
                 demonstrate one way to apply some of these advances in
                 a search-based planning prototype technique to
                 illustrate both the feasibility and the potential of
                 the proposed research. This strategy informs a number
                 of potentially interesting research directions and
                 problems. In the long term, this general technique
                 could enable sophisticated plan generation techniques
                 that improve domain specific knowledge, decrease human
                 effort, and increase the application of self-adaptive
  notes =        ", Java JAGP, PRISM, MAPE

                 School of Computer Science Carnegie Mellon University,
                 Pittsburgh, PA 15213


Genetic Programming entries for Zack Coker David Garlan Claire Le Goues