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.

• Type: Conference
• Year: 2006
• Where: ESOA
• Authors: A. E. Eiben, Mark Horvath, Wojtek Kowalczyk, Martijn C. Schut
• Keywords: Benchmark Ea, EA Parameters, ESOA 2006, Neural Networks, RL Procedure