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A surrogate model assisted (1+1)-ES with increased exploitation of the model

Published: 13 July 2019 Publication History

Abstract

Surrogate models in black-box optimization can be exploited to different degrees. At one end of the spectrum, they can be used to provide inexpensive but inaccurate assessments of the quality of candidate solutions generated by the black-box optimization algorithm. At the other end, optimization of the surrogate model function can be used in the process of generating those candidate solutions themselves. The latter approach more fully exploits the model, but may be more susceptible to systematic model error. This paper examines the effect of the degree of exploitation of the surrogate model in the context of a simple (1 + 1)-ES. First, we analytically derive the potential gain from more fully exploiting surrogate models by using a spherically symmetric test function and a simple model for the error resulting from the use of surrogate models. We then observe the effects of increased exploitation in an evolution strategy employing Gaussian process surrogate models applied to a range of test problems. We find that the gain resulting from more fully exploiting surrogate models can be considerable.

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Cited By

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  • (2023)Optimization of a Hydrodynamic Computational Reservoir through EvolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590355(202-210)Online publication date: 15-Jul-2023
  • (2023)Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art surveyEvolutionary Intelligence10.1007/s12065-023-00882-817:4(2213-2243)Online publication date: 3-Oct-2023
  • (2023)Surrogate-Assisted $$(1+1)$$-CMA-ES with Switching Mechanism of Utility FunctionsApplications of Evolutionary Computation10.1007/978-3-031-30229-9_51(798-814)Online publication date: 9-Apr-2023
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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference
July 2019
1545 pages
ISBN:9781450361118
DOI:10.1145/3321707
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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Publication History

Published: 13 July 2019

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Author Tags

  1. evolution strategy
  2. stochastic black-box optimization
  3. surrogate modelling

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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

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Cited By

View all
  • (2023)Optimization of a Hydrodynamic Computational Reservoir through EvolutionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590355(202-210)Online publication date: 15-Jul-2023
  • (2023)Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art surveyEvolutionary Intelligence10.1007/s12065-023-00882-817:4(2213-2243)Online publication date: 3-Oct-2023
  • (2023)Surrogate-Assisted $$(1+1)$$-CMA-ES with Switching Mechanism of Utility FunctionsApplications of Evolutionary Computation10.1007/978-3-031-30229-9_51(798-814)Online publication date: 9-Apr-2023
  • (2022)Monotone improvement of information-geometric optimization algorithms with a surrogate functionProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528690(1354-1362)Online publication date: 8-Jul-2022
  • (2022)Analysis of Surrogate-Assisted Information-Geometric Optimization AlgorithmsAlgorithmica10.1007/s00453-022-01087-886:1(33-63)Online publication date: 22-Dec-2022
  • (2022)Recombination Weight Based Selection in the DTS-CMA-ESParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_21(295-308)Online publication date: 15-Aug-2022
  • (2022)Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary OptimizationParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14714-2_6(76-89)Online publication date: 14-Aug-2022
  • (2020)Simple Surrogate Model Assisted Optimization with Covariance Matrix AdaptationParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58112-1_13(184-197)Online publication date: 31-Aug-2020

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