CITATION.cff

cff-version: 1.2.0
title: 'Evolver: Meta-optimizing multi-objective metaheuristics'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: José F.
    family-names: Aldana-Martín
    orcid: 'https://orcid.org/0000-0002-4845-762X'
    email: jfaldanam@uma.es
    affiliation: >-
      ITIS Software, Edificio de Investigación Ada Byron,
      University of Málaga, Málaga, 29071, Spain Dept. de
      Lenguajes y Ciencias de la Computación, University of
      Málaga, Málaga, 29071, Spain
  - family-names: Durillo
    given-names: 'Juan J. '
    email: durillo@lrz.de
    affiliation: >-
      Leibniz Supercomputing Centre of the Bavarian Academy
      of Sciences and Humanities, Garching bei Muenchen,
      Germany
    orcid: 'https://orcid.org/0000-0002-8023-6392'
  - given-names: Antonio J.
    family-names: Nebro
    email: ajnebro@uma.es
    affiliation: >-
      ITIS Software, Edificio de Investigación Ada Byron,
      University of Málaga, Málaga, 29071, Spain Dept. de
      Lenguajes y Ciencias de la Computación, University of
      Málaga, Málaga, 29071, Spain
    orcid: 'https://orcid.org/0000-0001-5580-0484'
identifiers:
  - type: doi
    value: 10.1016/j.softx.2023.101551
    description: Published DOI of the related paper
repository-code: 'https://github.com/jMetal/Evolver'
url: 'https://github.com/jMetal/Evolver/blob/main/README.md'
abstract: >-
  Evolver is a tool based on the formulation of the
  automatic configuration and design of multi-objective
  metaheuristics as a multi-objective optimization problem
  that can be solved by using the same kind of algorithms;
  i.e., we are applying a meta-optimization approach.
  Evolver provides highly configurable implementations of
  representative multi-objective solvers which can be
  automatically configured from a number of multi-objective
  problems used as the training set and a list of quality
  indicators which are the objectives to be optimized. Our
  tool is based on the jMetal framework, so a large number
  of existing algorithms can be used as meta-optimizers. A
  graphical user interface allows scientists to easily
  define auto-configuration scenarios, thus simplifying the
  complex process of finding high-quality algorithm
  settings.
keywords:
  - Multi-objective optimization
  - metaheuristics auto-configuration and auto-design
  - framework
  - jMetal
license: MIT
date-released: '2023-10-10'