---
res:
  bibo_abstract:
  - When selecting the best suited algorithm for an unknown optimization problem,
    it is useful to possess some a priori knowledge of the problem at hand. In the
    context of single-objective, continuous optimization problems such knowledge can
    be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically
    identifies properties of a landscape, e.g., the so-called funnel structures, based
    on an initial sample. In this paper, we extract the relevant features (for detecting
    funnels) out of a large set of landscape features when only given a small initial
    sample consisting of 50 x D observations, where D is the number of decision space
    dimensions. This is already in the range of the start population sizes of many
    evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used
    for training the classifier, and the approach is then very successfully validated
    on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching
    competition problems.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Pascal
      foaf_name: Kerschke, Pascal
      foaf_surname: Kerschke
  - foaf_Person:
      foaf_givenName: Mike
      foaf_name: Preuss, Mike
      foaf_surname: Preuss
  - foaf_Person:
      foaf_givenName: Simon
      foaf_name: Wessing, Simon
      foaf_surname: Wessing
  - 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: 10.1145/2908812.2908845
  dct_date: 2016^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/978-1-4503-4206-3
  dct_language: eng
  dct_title: Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models@
...