Optimization of control parameters for genetic algorithms
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Evolutionary Algorithms: The Role of Mutation and Recombination
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GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
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IEEE Transactions on Evolutionary Computation
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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.