%0 Conference Paper %1 eiben2006reinforcement-l %A Eiben, A.E. %A Horvath, M. %A Kowalczyk, W. %A and M.C. Schut, %B Proceedings of the 4th International Workshop on Engineering Self-Organizing Applications (ESOA'06) %D 2006 %E Brueckner, %E Hassas, %E Jelasity, %E Yamins, %K assignment, control, credit learning, meta-learning parameter rl, %T Reinforcement Learning for Online Control of Evolutionary Algorithms %U http://www.cs.vu.nl/%7Egusz/papers/ESOA-06-Eiben-Horvath-Kowalczyk-Schut.pdf %X 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 simul- taneously 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.

@inproceedings{eiben2006reinforcement-l, abstract = {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 simul- taneously 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.}, added-at = {2009-04-07T10:59:16.000+0200}, author = {Eiben, A.E. and Horvath, M. and Kowalczyk, W. and and M.C. Schut}, bdsk-file-1 = {YnBsaXN0MDDUAQIDBAUGCQpYJHZlcnNpb25UJHRvcFkkYXJjaGl2ZXJYJG9iamVjdHMSAAGGoNE HCFRyb290gAFfEA9OU0tleWVkQXJjaGl2ZXKoCwwXGBkdJCVVJG51bGzTDQ4PEBEUViRjbGFzc1 dOUy5rZXlzWk5TLm9iamVjdHOAB6ISE4ACgAOiFRaABIAGWWFsaWFzRGF0YVxyZWxhdGl2ZVBhd GjSDRobHFdOUy5kYXRhgAVPEQHsAAAAAAHsAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAA AADD42C9SCsAAAANTSweZWliZW4tMjAwNi1yZWluZm9yY2VtZW50LWwucGRmAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6zbcGMg8EAAAAAAAAAAAABAAMAAAkgAAAAAAAAAAAAAA AAAAAAA3NyYwAAEAAIAADD41KtAAAAEQAIAADBjHWxAAAAAQAYAA1NLAANTRwADU0YAAqkLwAKp BgAAHrcAAIAV01hY2ludG9zaCBIRDpVc2Vyczpzc3QyMTA6RG9jdW1lbnRzOlBhcGVyczpET1dO TE9BRFM6c3JjOmVpYmVuLTIwMDYtcmVpbmZvcmNlbWVudC1sLnBkZgAADgA+AB4AZQBpAGIAZQB uAC0AMgAwADAANgAtAHIAZQBpAG4AZgBvAHIAYwBlAG0AZQBuAHQALQBsAC4AcABkAGYADwAaAA wATQBhAGMAaQBuAHQAbwBzAGgAIABIAEQAEgBKVXNlcnMvc3N0MjEwL0RvY3VtZW50cy9QYXBlc nMvRE9XTkxPQURTL3NyYy9laWJlbi0yMDA2LXJlaW5mb3JjZW1lbnQtbC5wZGYAEwABLwAAFQAC AA3//wAA0h4fICFYJGNsYXNzZXNaJGNsYXNzbmFtZaMhIiNdTlNNdXRhYmxlRGF0YVZOU0RhdGF YTlNPYmplY3RfEC8uLi9ET1dOTE9BRFMvc3JjL2VpYmVuLTIwMDYtcmVpbmZvcmNlbWVudC1sLn BkZtIeHyYnoicjXE5TRGljdGlvbmFyeQAIABEAGgAfACkAMgA3ADoAPwBBAFMAXABiAGkAcAB4A IMAhQCIAIoAjACPAJEAkwCdAKoArwC3ALkCqQKuArcCwgLGAtQC2wLkAxYDGwMeAAAAAAAAAgEA AAAAAAAAKAAAAAAAAAAAAAAAAAAAAys=}, bdsk-url-1 = {http://www.cs.vu.nl/ci/ec/catalog/src/eiben-2006-reinforcement-l.pdf}, biburl = {https://www.bibsonomy.org/bibtex/235d0cf526e9355f7d3d26385bfeefd88/selmarsmit}, booktitle = {Proceedings of the 4th International Workshop on Engineering Self-Organizing Applications (ESOA'06)}, date-added = {2008-05-06 15:43:42 +0200}, date-modified = {2008-05-16 11:35:59 +0200}, description = {Selmar}, editor = {Brueckner and Hassas and Jelasity and Yamins}, interhash = {e5b868bc61a59210aa9491461f7ae29f}, intrahash = {35d0cf526e9355f7d3d26385bfeefd88}, keywords = {assignment, control, credit learning, meta-learning parameter rl,}, timestamp = {2009-04-07T10:59:19.000+0200}, title = {Reinforcement Learning for Online Control of Evolutionary Algorithms}, url = {http://www.cs.vu.nl/%7Egusz/papers/ESOA-06-Eiben-Horvath-Kowalczyk-Schut.pdf}, year = 2006 }