Using Automated Algorithm Configuration for Parameter Control

Deyao Chen, Maxim Buzdalov, Carola Doerr, Nguyen Dang

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion. This question has received considerable attention from the evolutionary community in recent years. Having a good benchmark collection to gain structural understanding on the effectiveness and limitations of different solution methods for DAC is therefore strongly desirable. Following recent work on proposing DAC benchmarks with well-understood theoretical properties and ground truth information, in this work, we suggest as a new DAC benchmark the controlling of the key parameter λ in the (1 + (λ, λ)) Genetic Algorithm for solving OneMax problems. We conduct a study on how to solve the DAC problem via the use of (static) automated algorithm configuration on the benchmark, and propose techniques to significantly improve the performance of the approach. Our approach is able to consistently outperform the default parameter control policy of the benchmark derived from previous theoretical work on sufficiently large problem sizes. We also present new findings on the landscape of the parameter-control search policies and propose methods to compute stronger baselines for the benchmark via numerical approximations of the true optimal policies.

Original languageEnglish
Title of host publicationFOGA 2023 - Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms
PublisherAssociation for Computing Machinery, Inc
Pages38-49
Number of pages12
ISBN (Electronic)9798400702020
DOIs
Publication statusPublished - 30 Aug 2023
Event17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2023 - Potsdam, Germany
Duration: 30 Aug 202301 Sept 2023

Publication series

NameFOGA 2023 - Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms

Conference

Conference17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2023
Country/TerritoryGermany
CityPotsdam
Period30 Aug 202301 Sept 2023

Keywords

  • algorithm configuration
  • benchmarking
  • evolutionary computation
  • genetic algorithms
  • parameter control

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