Genetic Improvement of Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "William B. Langdon",
  title =        "Genetic Improvement of Programs",
  booktitle =    "16th International Symposium on Symbolic and Numeric
                 Algorithms for Scientific Computing (SYNASC 2014)",
  year =         "2014",
  editor =       "Franz Winkler and Viorel Negru and Tetsuo Ida and 
                 Tudor Jebelean and Dana Petcu and Stephen Watt and 
                 Daniela Zaharie",
  pages =        "14--19",
  address =      "Timisoara",
  month =        "22-25 " # sep,
  organisation = "Department of Computer Science, West University of
                 Timisoara, Romania; + Research Institute for Symbolic
                 Computation, Johannes Kepler University, Linz, Austria;
                 + Research Institute e-Austria, Timisoara, Romania.",
  publisher =    "IEEE",
  note =         "Keynote",
  keywords =     "genetic algorithms, genetic programming, genetic
                 improvement, GI, Automatic software re-engineering,
                 Bowtie2GP, multiple objective exploration, search based
                 software engineering (SBSE), GPGPU",
  isbn13 =       "978-1-4799-8448-0",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/SYNASC.2014.10",
  size =         "6 pages",
  abstract =     "Genetic programming can optimise software, including:
                 evolving test benchmarks, generating hyper-heuristics
                 by searching meta-heuristics, generating communication
                 protocols, composing telephony systems and web
                 services, generating improved hashing and C++ heap
                 managers, redundant programming and even automatic bug
                 fixing. Particularly in embedded real-time or mobile
                 systems, there may be many ways to trade off expenses
                 (such as time, memory, energy, power consumption) vs.
                 functionality. Human programmers cannot try them all.
                 Also the best multi-objective Pareto trade off may
                 change with time, underlying hardware and network
                 connection or user behaviour. It may be GP can
                 automatically suggest different trade offs for each new
                 market. Recent results include substantial speed up by
                 evolving a new version of a program customised for a
                 special case.",
  notes =        "
                 CPS Also known as \cite{7034660}",

Genetic Programming entries for William B Langdon