Predict the Success or Failure of an Evolutionary Algorithm Run

Azad, Raja Muhammad Atif and Chennupati, Gopinath and Ryan, Conor (2014) Predict the Success or Failure of an Evolutionary Algorithm Run. In: The proceedings of Genetic and Evolutionary Computation Conference, GECCO '14. ACM, pp. 131-132. ISBN 978-1-4503-2881-4/14/07

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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 Optimization (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.

Item Type: Book Section
Uncontrolled Keywords: Grammatical Evolution, AntMining, Machine Learning, Symbolic Regression, Training Set.
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Enterprise Systems
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 12 Jun 2017 12:57
Last Modified: 11 Oct 2017 07:56
URI: http://www.open-access.bcu.ac.uk/id/eprint/4604

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