Predict the performance of GE with an ACO based machine learning algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Gopinath Chennupati and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "Predict the performance of GE with an ACO based
                 machine learning algorithm",
  booktitle =    "GECCO 2014 Workshop on Symbolic Regression and
  year =         "2014",
  editor =       "Steven Gustafson and Ekaterina Vladislavleva",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
  pages =        "1353--1360",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2598394.2609860",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The quality of the evolved solutions of an
                 evolutionary algorithm (EA) varies across different
                 runs and a significant percentage of runs can produce
                 solutions of undesirable quality. These runs are a
                 waste of computational resources, particularly in
                 difficult problems where practitioners have time bound
                 limitations in repeating runs. This paper proposes a
                 completely novel approach, that of a Run Prediction
                 Model (RPM) in which we identify and terminate
                 evolutionary runs that are likely to produce
                 low-quality solutions. This is justified with an Ant
                 Colony Optimization (ACO) based classifier that learns
                 from the early generations of a run and decides whether
                 to continue or not.

                 We apply RPM to Grammatical Evolution (GE) applied to
                 four benchmark symbolic regression problems and
                 consider several contemporary machine learning
                 algorithms to train the predictive models and find that
                 ACO produces the best results and acceptable predictive
                 accuracy for this first investigation. The ACO
                 discovered prediction models are in the form of a list
                 of simple rules. We further analyse that list manually
                 to tune them in order to predict poor GE runs.

                 We then apply the analysed model to GE runs on the
                 regression problems and terminate the runs identified
                 by the model likely to be poor, thus increasing the
                 rate of production of successful runs while reducing
                 the computational effort required. We demonstrate that,
                 although there is a high bootstrapping cost for RPM,
                 further investigation is warranted as the mean success
                 rate and the total execution time enjoys a
                 statistically significant boost on all the four
                 benchmark problems.",
  notes =        "Also known as \cite{2609860} Distributed at

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