---
res:
  bibo_abstract:
  - This paper addresses multimodality of multi-objective (MO) optimization landscapes.
    Contrary to common perception of local optima, according to which they are hindering
    the progress of optimization algorithms, it will be shown that local efficient
    sets in a multi-objective setting can assist optimizers in finding global efficient
    sets. We use sophisticated visualization techniques, which rely on gradient field
    heatmaps, to highlight those insights into landscape characteristics. Finally,
    the MO local optimizer MOGSA is introduced, which exploits those observations
    by sliding down the multi-objective gradient hill and moving along the local efficient
    sets.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christian
      foaf_name: Grimme, Christian
      foaf_surname: Grimme
  - foaf_Person:
      foaf_givenName: Pascal
      foaf_name: Kerschke, Pascal
      foaf_surname: Kerschke
  - 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: 10.1007/978-3-030-12598-1_11
  bibo_volume: 11411
  dct_date: 2019^xs_gYear
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Multimodality in Multi-Objective Optimization — More Boon than Bane?@
...