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2006

Reinforcement Learning for Online Control of Evolutionary Algorithms

8 years 9 months ago
Reinforcement Learning for Online Control of Evolutionary Algorithms
The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We evaluate this approach experimentally on a range of fitness landscapes with varying degrees of ruggedness. The results show that EA calibrated by the RL-based approach outperforms a benchmark EA.
A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Marti
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ESOA
Authors A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Martijn C. Schut
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