---
res:
  bibo_abstract:
  - <jats:p>Optimization algorithms contain parameters that greatly influence their
    behavior. Finding the right settings for parameters through automated algorithm
    configuration has become a critical component of designing competitive algorithms.
    While traditional offline configurators tackle this problem by finding one configuration
    that works well for a set of instances, instance-specific algorithm configuration
    utilizes features of the instances to provide configurations that are tailored
    to each instance to maximize performance. We propose the first instance-specific
    algorithm configurator based on deep reinforcement learning that can be used in
    general algorithm configuration settings. Our method is able to handle large,
    mixed, discrete and continuous search spaces and only requires a small number
    of instances for training. Not only does it select an individual configuration
    for every instance, it also selects configurations from a much broader range.
    We show that our configurator provides improvements over the state-of-the-art
    instance-specific configurators ISAC++ and Hydra on a wide range of problem domains.</jats:p>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Elias
      foaf_name: Schede, Elias
      foaf_surname: Schede
  - foaf_Person:
      foaf_givenName: Moritz Vinzent
      foaf_name: Seiler, Moritz Vinzent
      foaf_surname: Seiler
  - foaf_Person:
      foaf_givenName: Carolin
      foaf_name: Mensendiek, Carolin
      foaf_surname: Mensendiek
      foaf_workInfoHomepage: http://www.librecat.org/personId=75006
  - foaf_Person:
      foaf_givenName: Kevin
      foaf_name: Tierney, Kevin
      foaf_surname: Tierney
  - foaf_Person:
      foaf_givenName: Heike
      foaf_name: Trautmann, Heike
      foaf_surname: Trautmann
      foaf_workInfoHomepage: http://www.librecat.org/personId=100740
    orcid: 0000-0002-9788-8282
  bibo_doi: 10.1145/3844954
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2688-299X
  - http://id.crossref.org/issn/2688-3007
  dct_language: eng
  dct_publisher: Association for Computing Machinery (ACM)@
  dct_title: 'OPTICAT: A Deep Reinforcement Learning Framework for Instance-Specific
    Algorithm Configuration@'
...