- 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 Modelling",
- year = "2014",
- editor = "Steven Gustafson and Ekaterina Vladislavleva",
- isbn13 = "978-1-4503-2881-4",
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
- pages = "1353--1360",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Vancouver, BC, Canada",
-
URL = "
http://doi.acm.org/10.1145/2598394.2609860",
-
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 GECCO-2014.",
