TY - GEN
T1 - Predict the success or failure of an evolutionary algorithm run
AU - Chennupati, Gopinath
AU - Ryan, Conor
AU - Azad, R. Muhammad Atif
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Ant mining
KW - Grammatical Evolution
KW - Machine learning
KW - Symbolic regression
KW - Training set
UR - http://www.scopus.com/inward/record.url?scp=84905641247&partnerID=8YFLogxK
U2 - 10.1145/2598394.2598471
DO - 10.1145/2598394.2598471
M3 - Conference contribution
AN - SCOPUS:84905641247
SN - 9781450328814
T3 - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
SP - 131
EP - 132
BT - GECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery
T2 - 16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Y2 - 12 July 2014 through 16 July 2014
ER -