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Onemax helps optimizing XdivK:: theoretical runtime analysis for RLS and EA+RL

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 201 - 202

https://doi.org/10.1145/2598394.2598442

Published: 12 July 2014 Publication History

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Abstract

There exist optimization problems with the target objective, which is to be optimized, and several extra objectives, which can be helpful in the optimization process. The previously proposed EA+RL method is designed to adaptively select objectives during the run of an optimization algorithm in order to reduce the number of evaluations needed to reach an optimum of the target objective.

The case when the extra objective is a fine-grained version of the target one is probably the simplest case when using an extra objective actually helps. We define a coarse-grained version of OneMax called XdivK as follows: XdivK(x)= [OneMax(x)/k] for a parameter k which is a divisor of n- the length of a bit vector. We also define XdivK+OneMax, which is a problem where the target objective is XdivK and a single extra objective is OneMax.

In this paper, the randomized local search (RLS) is used in the EA+RL method as an optimization algorithm. We construct exact expressions for the expected running time of RLS solving the XdivK problem and of the EA+RL method solving the XdivK+OneMax problem. It is shown that the EA+RL method makes optimization faster, and the speedup is exponential in k.

References

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Cited By

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Ma XHuang ZLi XQi YWang LZhu Z(2023)Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A SurveyIEEE Transactions on Cybernetics10.1109/TCYB.2021.312078853:6(3702-3715)Online publication date: Jun-2023
https://doi.org/10.1109/TCYB.2021.3120788
Antipov DBuzdalova AStankevich ATakadama K(2018)Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208231(1886-1889)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208231
Petrova IBuzdalova A(2017)Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082499(1435-1438)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082499
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Onemax helps optimizing XdivK:: theoretical runtime analysis for RLS and EA+RL

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GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 12 July 2014

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GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Ma XHuang ZLi XQi YWang LZhu Z(2023)Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A SurveyIEEE Transactions on Cybernetics10.1109/TCYB.2021.312078853:6(3702-3715)Online publication date: Jun-2023
https://doi.org/10.1109/TCYB.2021.3120788
Antipov DBuzdalova AStankevich ATakadama K(2018)Runtime analysis of a population-based evolutionary algorithm with auxiliary objectives selected by reinforcement learningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208231(1886-1889)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208231
Petrova IBuzdalova A(2017)Reinforcement learning based dynamic selection of auxiliary objectives with preservation of the best found solutionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082499(1435-1438)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082499
Antipov DBuzdalova A(2017)Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969567(2169-2176)Online publication date: 5-Jun-2017
https://dl.acm.org/doi/10.1109/CEC.2017.7969567
Polevaya TBuzdalov M(2016)Preserving diversity in auxiliary objectives provably speeds up crossing plateaus2016 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2016.7850145(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/SSCI.2016.7850145
Buzdalov MBuzdalova A(2015)Can OneMax help optimizing LeadingOnes using the EA+RL method?2015 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2015.7257100(1762-1768)Online publication date: May-2015
https://doi.org/10.1109/CEC.2015.7257100
Buzdalov M(2014)A Switch-and-Restart Algorithm with Exponential Restart Strategy for Objective Selection and its Runtime AnalysisProceedings of the 2014 13th International Conference on Machine Learning and Applications10.1109/ICMLA.2014.27(141-146)Online publication date: 3-Dec-2014
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Buzdalova ABuzdalov M(2014)A New Algorithm for Adaptive Online Selection of Auxiliary ObjectivesProceedings of the 2014 13th International Conference on Machine Learning and Applications10.1109/ICMLA.2014.100(584-587)Online publication date: 3-Dec-2014
https://dl.acm.org/doi/10.1109/ICMLA.2014.100

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