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On benchmark properties for adaptive operator selection

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2217 - 2218

https://doi.org/10.1145/1570256.1570306

Published: 08 July 2009 Publication History

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References

[1]

L. DaCosta, A. Fialho, M. Schoenauer, and M. Sebag. Adaptive operator selection with dynamic multiarmed bandits. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 913-920, New York, NY, USA, 2008. ACM.

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A. Fialho, L. Da Costa, M. Schoenauer, and M. Sebag. Extreme Value Based Adaptive Operator Selection. In PPSN08, volume 5199 of LNCS, pages 175-184. Springer, 2008.

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A. Fialho, L. Da Costa, M. Schoenauer, and M. Sebag. Dynamic multi-armed bandits and extreme value-based rewards for adaptive operator selection in evolutionary algorithms. In LION'09: Proceedings of the 3rd International Conference on Learning and Intelligent Optimization (to appear). SV, 2009.

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D. Thierens. An adaptive pursuit strategy for allocating operator probabilities. In GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, pages 1539-1546, New York, NY, USA, 2005. ACM.

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Cited By

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Santana RShakya S(2022)Evolutionary approaches with adaptive operators for the bi-objective TTP2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022162(1202-1209)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022162
Buskulic NDoerr C(2021)Maximizing Drift is Not Optimal for Solving OneMaxEvolutionary Computation10.1162/evco_a_00290(1-20)Online publication date: 22-Jan-2021
https://doi.org/10.1162/evco_a_00290
Doerr CYe FHoresh NWang HShir OBäck T(2020)Benchmarking discrete optimization heuristics with IOHprofilerApplied Soft Computing10.1016/j.asoc.2019.10602788:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.asoc.2019.106027
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Index Terms

On benchmark properties for adaptive operator selection
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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adaptive operator selection

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Santana RShakya S(2022)Evolutionary approaches with adaptive operators for the bi-objective TTP2022 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI51031.2022.10022162(1202-1209)Online publication date: 4-Dec-2022
https://doi.org/10.1109/SSCI51031.2022.10022162
Buskulic NDoerr C(2021)Maximizing Drift is Not Optimal for Solving OneMaxEvolutionary Computation10.1162/evco_a_00290(1-20)Online publication date: 22-Jan-2021
https://doi.org/10.1162/evco_a_00290
Doerr CYe FHoresh NWang HShir OBäck T(2020)Benchmarking discrete optimization heuristics with IOHprofilerApplied Soft Computing10.1016/j.asoc.2019.10602788:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.asoc.2019.106027
Buzdalov MDoerr C(2020)Optimal Mutation Rates for the $$(1+\lambda )$$ EA on OneMaxParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_40(574-587)Online publication date: 2-Sep-2020
https://doi.org/10.1007/978-3-030-58115-2_40
Rodionova AAntonov KBuzdalova ADoerr CAuger AStützle T(2019)Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation ratesProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321827(855-863)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3321707.3321827
Buskulic NDoerr CAuger AStützle T(2019)Maximizing drift is not optimal for solving OneMaxProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3321952(425-426)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3321952
Doerr CWagner MTakadama K(2018)Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205560(943-950)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205560
Doerr CWagner M(2018)Sensitivity of Parameter Control Mechanisms with Respect to Their InitializationParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99259-4_29(360-372)Online publication date: 21-Aug-2018
https://doi.org/10.1007/978-3-319-99259-4_29

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Non stationary operator selection with island models

Landscape-based adaptive operator selection mechanism for differential evolution

Adaptive operator selection with dueling deep Q-network for evolutionary multi-objective optimization

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