Predict the success or failure of an evolutionary algorithm run

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

  author =       "Gopinath Chennupati and Conor Ryan and 
                 R. Muhammad Atif Azad",
  title =        "Predict the success or failure of an evolutionary
                 algorithm run",
  booktitle =    "GECCO Comp '14: Proceedings of the 2014 conference
                 companion on Genetic and evolutionary computation
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution: Poster",
  pages =        "131--132",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2598394.2598471",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The quality of candidate solutions in evolutionary
                 computation (EC) depend on multiple independent runs
                 and a large number of them fail to guarantee optimal
                 result. These runs consume more or less equal or
                 sometimes higher amount of computational resources on
                 par the runs that produce desirable results.

                 This research work addresses these two issues (run
                 quality, execution time), Run Prediction Model (RPM),
                 in which undesirable quality evolutionary runs are
                 identified to discontinue from their execution. An Ant
                 Colony Optimisation (ACO) based classifier that learns
                 to discover a prediction model from the early
                 generations of an EC run.

                 We consider Grammatical Evolution (GE) as our EC
                 technique to apply RPM that is evaluated on four
                 symbolic regression problems. We establish that the RPM
                 applied GE produces a significant improvement in the
                 success rate while reducing the execution time.",
  notes =        "Also known as \cite{2598471} Distributed at

Genetic Programming entries for Gopinath Chennupati Conor Ryan R Muhammad Atif Azad