- 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 companion",
- 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 = "
http://doi.acm.org/10.1145/2598394.2598471",
-
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 GECCO-2014.",
