research-article

Adaptive population models for offspring populations and parallel evolutionary algorithms

Authors:

Dirk SudholtAuthors Info & Claims

FOGA '11: Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms

Pages 181 - 192

https://doi.org/10.1145/1967654.1967671

Published: 05 January 2011 Publication History

Abstract

We present two adaptive schemes for dynamically choosing the number of parallel instances in parallel evolutionary algorithms. This includes the choice of the offspring population size in a (1+λ) EA as a special case. Our schemes are parameterless and they work in a black-box setting where no knowledge on the problem is available. Both schemes double the number of instances in case a generation ends without finding an improvement. In a successful generation, the first scheme resets the system to one instance, while the second scheme halves the number of instances. Both schemes provide near-optimal speed-ups in terms of the parallel time. We give upper bounds for the asymptotic sequential time (i.e., the total number of function evaluations) that are not larger than upper bounds for a corresponding non-parallel algorithm derived by the fitness-level method.

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Cited By

Guan XYang TZhao CZhou YLarson K(2024)Feedback-based adaptive crossover-rate in evolutionary computationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/765(6923-6930)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/765
Antipov DDoerr BIvanova ALi XHandl J(2024)Already Moderate Population Sizes Provably Yield Strong Robustness to NoiseProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654196(1524-1532)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654196
Kaufmann MLarcher MLengler JSieberling O(2024)Hardest Monotone Functions for Evolutionary AlgorithmsEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-57712-3_10(146-161)Online publication date: 2024
https://doi.org/10.1007/978-3-031-57712-3_10
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Index Terms

Adaptive population models for offspring populations and parallel evolutionary algorithms
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

FOGA '11: Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms

January 2011

262 pages

ISBN:9781450306331

DOI:10.1145/1967654

Program Chairs:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
W. B. Langdon
University College London, UK

Copyright © 2011 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 05 January 2011

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FOGA '11

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SIGEVO

FOGA '11: Foundations of Genetic Algorithms XI

January 5 - 9, 2011

Schwarzenberg, Austria

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Cited By

Guan XYang TZhao CZhou YLarson K(2024)Feedback-based adaptive crossover-rate in evolutionary computationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/765(6923-6930)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/765
Antipov DDoerr BIvanova ALi XHandl J(2024)Already Moderate Population Sizes Provably Yield Strong Robustness to NoiseProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654196(1524-1532)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654196
Kaufmann MLarcher MLengler JSieberling O(2024)Hardest Monotone Functions for Evolutionary AlgorithmsEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-57712-3_10(146-161)Online publication date: 2024
https://doi.org/10.1007/978-3-031-57712-3_10
Rajabi AWitt C(2023)Stagnation Detection with Randomized Local Search*Evolutionary Computation10.1162/evco_a_0031331:1(1-29)Online publication date: 1-Mar-2023
https://doi.org/10.1162/evco_a_00313
Jorritsma JLengler JSudholt DSilva SPaquete L(2023)Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted OptimaProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590488(1602-1610)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590488
Hevia Fajardo MSudholt D(2023)Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic AlgorithmACM Transactions on Evolutionary Learning and Optimization10.1145/35647552:4(1-39)Online publication date: 14-Jan-2023
https://dl.acm.org/doi/10.1145/3564755
Hevia Fajardo MSudholt D(2023)Self-Adjusting Offspring Population Sizes Outperform Fixed Parameters on the Cliff FunctionArtificial Intelligence10.1016/j.artint.2023.104061(104061)Online publication date: Dec-2023
https://doi.org/10.1016/j.artint.2023.104061
Hevia Fajardo MSudholt D(2023)Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates MatterAlgorithmica10.1007/s00453-023-01153-986:2(526-565)Online publication date: 24-Jul-2023
https://doi.org/10.1007/s00453-023-01153-9
Hevia Fajardo MSudholt DWagner M(2022)Hard problems are easier for success-based parameter controlProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528781(796-804)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3512290.3528781
Rajabi AWitt C(2022)Self-Adjusting Evolutionary Algorithms for Multimodal OptimizationAlgorithmica10.1007/s00453-022-00933-z84:6(1694-1723)Online publication date: 16-Feb-2022
https://doi.org/10.1007/s00453-022-00933-z
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