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GECCO
2000
Springer

Modeling GA Performance for Control Parameter Optimization

13 years 7 months ago
Modeling GA Performance for Control Parameter Optimization
Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control parameter optimization. We enhance this process by incorporating a neural network for tness evaluation. This neural network is trained to learn the complex interactions of the genetic algorithm control parameters and is used to predict the performance of the genetic algorithm relative to values of these control parameters. To validate our approach we describe a genetic algorithmfor the largest commonsubgraph problemthatwe develop usingthisneural network enhanced meta-level genetic algorithm. The resulting genetic algorithm signi cantly outperforms a hand-tuned variant and is shown to be competitive with a hill-climbing algorithm used in practical applications.
Vincent A. Cicirello, Stephen F. Smith
Added 24 Aug 2010
Updated 24 Aug 2010
Type Conference
Year 2000
Where GECCO
Authors Vincent A. Cicirello, Stephen F. Smith
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