MATE: A Model-Based Algorithm Tuning Engine: A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression

Mohamed El Yafrani*, Marcella Scoczynski, Inkyung Sung, Markus Wagner, Carola Doerr, Peter Nielsen

*Corresponding author

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Original languageEnglish
Title of host publicationEvolutionary Computation in Combinatorial Optimization - 21st European Conference, EvoCOP 2021, Held as Part of EvoStar 2021, Proceedings
EditorsChristine Zarges, Sébastien Verel
Number of pages17
PublisherSpringer Science+Business Media
Publication date2021
Pages51-67
ISBN (Print)9783030729035
DOIs
Publication statusPublished - 2021
Event21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021 Held as Part of EvoStar 2021 - Virtual, Online
Duration: 7 Apr 20219 Apr 2021

Conference

Conference21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021 Held as Part of EvoStar 2021
CityVirtual, Online
Period07/04/202109/04/2021
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12692 LNCS
ISSN0302-9743

Bibliographical note

Funding Information:
M. Martins acknowledges CNPq (Brazil Government). M. Wagner acknowledges the ARC Discovery Early Career Researcher Award DE160100850. C. Doerr acknowledges support from the Paris Ile-de-France Region. Experiments were performed on the AAU?s CLAUDIA compute cloud platform.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Genetic programming
  • Model-based tuning
  • Parameter tuning

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